diff --git a/jetson_containers/build.py b/jetson_containers/build.py index 4994b85f1..83a362512 100644 --- a/jetson_containers/build.py +++ b/jetson_containers/build.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 # -# Container build system for managing package configurations and multi-stage build chains, with automated testing and dependency tracking. +# Container build system for managing package configurations and multi-stage build chains, with automated testing and dependency tracking. # # A "package" is composed of a Dockerfile, configs, and test scripts. These are found under the jetson-containers/packages directory. # There are also "meta-packages" that have no Dockerfiles themselves, but specify a set of packages to include (e.g. l4t-pytorch) @@ -12,9 +12,9 @@ # Some example build scenarios: # # $ jetson-containers/build.sh --name=xyz pytorch jupyterlab # build container with PyTorch and JupyterLab server -# $ jetson-containers/build.sh --multiple pytorch tensorflow # build separate containers for PyTorch and +# $ jetson-containers/build.sh --multiple pytorch tensorflow # build separate containers for PyTorch and # $ jetson-containers/build.sh --multiple ros:humble* # build all ROS Humble containers (can use wildcards) -# $ jetson-containers/build.sh ros:humble-desktop pytorch # build ROS Humble with PyTorch on top +# $ jetson-containers/build.sh ros:humble-desktop pytorch # build ROS Humble with PyTorch on top # $ jetson-containers/build.sh --base=xyz:latest pytorch # add PyTorch to an existing container # # Typically the jetson-containers/build.sh wrapper script is used to launch this underlying Python module. jetson-containers can also @@ -32,7 +32,7 @@ ) parser = argparse.ArgumentParser() - + parser.add_argument('packages', type=str, nargs='*', default=[], help='packages or containers to build (filterable by wildcards)') parser.add_argument('--name', type=str, default='', help="the name of the output container to build") @@ -57,15 +57,18 @@ parser.add_argument('--version', action='store_true', help="print platform version info and exit") parser.add_argument('--no-github-api', action='store_true', help="disalbe Github API use to force rebuild on new git commits") +parser.add_argument('--build-network', type=str, default='host', help="Docker network mode to use during build (default: host)") +parser.add_argument('--test-network', type=str, default='host', help="Docker network mode to use during test phase (default: host)") + args = parser.parse_args() # validate args if args.skip_errors and not args.multiple: raise ValueError("--skip-errors can only be used with --multiple flag") - + if args.verbose: os.environ['VERBOSE'] = 'ON' - + # split multi-value keyword arguments args.package_dirs = re.split(',|;|:', args.package_dirs) args.skip_packages = re.split(',|;|:', args.skip_packages) @@ -103,7 +106,7 @@ # set logging directories if args.logs: set_log_dir(args.logs) - + # list/show package info if args.list_packages or args.show_packages: packages = find_packages(args.packages, skip=args.skip_packages) @@ -111,15 +114,19 @@ if args.list_packages: for package in sorted(packages.keys()): print(package) - + if args.show_packages: pprint.pprint(packages) - + sys.exit(0) - + # build one multi-stage container from chain of packages # or launch multiple independent container builds if not args.multiple: - build_container(args.name, args.packages, args.base, args.build_flags, args.build_args, args.simulate, args.skip_tests, args.test_only, args.push, args.no_github_api, args.skip_packages) -else: - build_containers(args.name, args.packages, args.base, args.build_flags, args.build_args, args.simulate, args.skip_errors, args.skip_packages, args.skip_tests, args.test_only, args.push) + build_container(args.name, args.packages, args.base, args.build_flags, args.build_args, args.simulate, args.skip_tests, args.test_only, args.push, args.no_github_api, args.skip_packages, + build_network=args.build_network, + test_network=args.test_network ) +else: + build_containers(args.name, args.packages, args.base, args.build_flags, args.build_args, args.simulate, args.skip_errors, args.skip_packages, args.skip_tests, args.test_only, args.push, + build_network=args.build_network, + test_network=args.test_network ) diff --git a/jetson_containers/container.py b/jetson_containers/container.py index 0435f1932..572277c44 100644 --- a/jetson_containers/container.py +++ b/jetson_containers/container.py @@ -9,7 +9,7 @@ import logging import traceback import subprocess -import dockerhub_api +import dockerhub_api from packaging.version import Version @@ -18,7 +18,7 @@ from .logging import log_dir, log_debug, pprint_debug from .l4t_version import ( - L4T_VERSION, LSB_RELEASES, l4t_version_from_tag, l4t_version_compatible, + L4T_VERSION, LSB_RELEASES, l4t_version_from_tag, l4t_version_compatible, get_l4t_base, get_cuda_arch, get_cuda_version, get_jetpack_version, get_lsb_release ) @@ -26,14 +26,14 @@ _NEWLINE_=" \\\n" # used when building command strings -def build_container(name, packages, base=get_l4t_base(), build_flags='', build_args=None, simulate=False, skip_tests=[], test_only=[], push='', no_github_api=False, skip_packages=[]): +def build_container(name, packages, base=get_l4t_base(), build_flags='', build_args=None, simulate=False, skip_tests=[], test_only=[], push='', no_github_api=False, skip_packages=[], build_network='host', test_network='host'): """ Multi-stage container build that chains together selected packages into one container image. For example, `['pytorch', 'tensorflow']` would build a container that had both pytorch and tensorflow in it. - + Parameters: name (str) -- name of container image to build (or a namespace to build under, ending in /) - if empty, a default name will be assigned based on the package(s) selected. + if empty, a default name will be assigned based on the package(s) selected. packages (list[str]) -- list of package names to build (into one container) base (str) -- base container image to use (defaults to l4t-base or l4t-jetpack) build_flags (str) -- arguments to add to the 'docker build' command @@ -42,51 +42,53 @@ def build_container(name, packages, base=get_l4t_base(), build_flags='', build_a test_only (list[str]) -- only test these specified packages, skipping all other tests push (str) -- name of repository or user to push container to (no push if blank) no_github_api (bool) -- if true, use custom Dockerfile with no `ADD https://api.github.com/repos/...` line. - - Returns: + build_network -- Docker network mode to use during build (default: host) + test_network -- Docker network mode to use during test (default: host) + + Returns: The full name of the container image that was built (as a string) - + """ if isinstance(packages, str): packages = [packages] elif validate_dict(packages): packages = [packages['name']] - + if len(packages) == 0: - raise ValueError("must specify at least one package to build") - + raise ValueError("must specify at least one package to build") + # by default these have an empty string if len(skip_tests) == 1 and len(skip_tests[0]) == 0: skip_tests = [] - + if len(test_only) == 1 and len(test_only[0]) == 0: test_only = [] # get default base container (l4t-jetpack) if not base: base = get_l4t_base() - + # add all dependencies to the build tree packages = resolve_dependencies(packages, skip_packages=skip_packages) print('-- Building containers ', packages) - + # make sure all packages can be found before building any - for package in packages: + for package in packages: find_package(package) - + # assign default container repository if needed - if len(name) == 0: + if len(name) == 0: name = packages[-1] elif name.find(':') < 0 and name[-1] == '/': # they gave a namespace to build under name += packages[-1] - + # add prefix to tag last_pkg = find_package(packages[-1]) prefix = last_pkg.get('prefix', '') postfix = last_pkg.get('postfix', '') - + tag_idx = name.find(':') - + if prefix: if tag_idx >= 0: name = name[:tag_idx+1] + prefix + '-' + name[tag_idx+1:] @@ -103,12 +105,12 @@ def build_container(name, packages, base=get_l4t_base(), build_flags='', build_a # generate the logging file (without the extension) log_file = os.path.join(log_dir('build'), container_name.replace('/','_')).replace(':','_') - + # build next intermediate container pkg = find_package(package) - + if 'dockerfile' in pkg: - cmd = f"{sudo_prefix()}DOCKER_BUILDKIT=0 docker build --network=host --tag {container_name}" + _NEWLINE_ + cmd = f"{sudo_prefix()}DOCKER_BUILDKIT=0 docker build --network={build_network} --tag {container_name}" + _NEWLINE_ if no_github_api: dockerfilepath = os.path.join(pkg['path'], pkg['dockerfile']) with open(dockerfilepath, 'r') as fp: @@ -123,71 +125,71 @@ def build_container(name, packages, base=get_l4t_base(), build_flags='', build_a else: cmd += f"--file {os.path.join(pkg['path'], pkg['dockerfile'])}" + _NEWLINE_ cmd += f"--build-arg BASE_IMAGE={base}" + _NEWLINE_ - + if 'build_args' in pkg: cmd += ''.join([f"--build-arg {key}=\"{value}\"" + _NEWLINE_ for key, value in pkg['build_args'].items()]) if build_args: for key, value in build_args.items(): - cmd += f"--build-arg {key}={value}" + _NEWLINE_ + cmd += f"--build-arg {key}={value}" + _NEWLINE_ if 'build_flags' in pkg: cmd += pkg['build_flags'] + _NEWLINE_ - + if build_flags: cmd += build_flags + _NEWLINE_ - + cmd += pkg['path'] + _NEWLINE_ #" . " cmd += f"2>&1 | tee {log_file + '.txt'}" + "; exit ${PIPESTATUS[0]}" # non-tee version: https://stackoverflow.com/a/34604684 - + print(f"-- Building container {container_name}") print(f"\n{cmd}\n") with open(log_file + '.sh', 'w') as cmd_file: # save the build command to a shell script for future reference cmd_file.write('#!/usr/bin/env bash\n\n') cmd_file.write(cmd + '\n') - - if not simulate: # remove the line breaks that were added for readability, and set the shell to bash so we can use $PIPESTATUS - status = subprocess.run(cmd.replace(_NEWLINE_, ' '), executable='/bin/bash', shell=True, check=True) + + if not simulate: # remove the line breaks that were added for readability, and set the shell to bash so we can use $PIPESTATUS + status = subprocess.run(cmd.replace(_NEWLINE_, ' '), executable='/bin/bash', shell=True, check=True) else: tag_container(base, container_name, simulate) - + # run tests on the intermediate container if package not in skip_tests and 'intermediate' not in skip_tests and 'all' not in skip_tests: if len(test_only) == 0 or package in test_only: - test_container(container_name, pkg, simulate) - + test_container(container_name, pkg, simulate, test_network) + # use this container as the next base base = container_name # tag the final container tag_container(container_name, name, simulate) - + # re-run tests on final container for package in packages: if package not in skip_tests and 'all' not in skip_tests: if len(test_only) == 0 or package in test_only: - test_container(name, package, simulate) - + test_container(name, package, simulate, test_network) + # push container if push: push_container(name, push, simulate) - - print(f"-- Done building container {name}") + + print(f"-- Done building container {name}") return name - - -def build_containers(name, packages, base=get_l4t_base(), build_flags='', build_args=None, simulate=False, skip_errors=False, skip_packages=[], skip_tests=[], test_only=[], push=''): + + +def build_containers(name, packages, base=get_l4t_base(), build_flags='', build_args=None, simulate=False, skip_errors=False, skip_packages=[], skip_tests=[], test_only=[], push='', build_network='host', test_network='host'): """ Build separate container images for each of the requested packages (this is typically used in batch building jobs) For example, `['pytorch', 'tensorflow']` would build a pytorch container and a tensorflow container. - + TODO add multiprocessing parallel build support for jobs=-1 (use all CPU cores) - + Parameters: name (str) -- name of container to build (or a namespace to build under, ending in /) if empty, a default name will be assigned based on the package(s) selected. - wildcards can be used to select packages (i.e. 'ros*' would build all ROS packages) + wildcards can be used to select packages (i.e. 'ros*' would build all ROS packages) packages (list[str]) -- list of package names to build (in separated containers) base (str) -- base container image to use (defaults to l4t-base or l4t-jetpack) build_flags (str) -- arguments to add to the 'docker build' command @@ -197,21 +199,23 @@ def build_containers(name, packages, base=get_l4t_base(), build_flags='', build_ skip_tests (list[str]) -- list of tests to skip (or 'all' or 'intermediate') test_only (list[str]) -- only test these specified packages, skipping all other tests push (str) -- name of repository or user to push container to (no push if blank) - - Returns: + build_network -- Docker network mode to use during build (default: host) + test_network -- Docker network mode to use during test (default: host) + + Returns: True if all containers built successfully, or False if there were any errors. """ if not packages: # build everything (for testing) packages = sorted(find_packages([]).keys()) - + packages = find_packages(packages, skip=skip_packages) print('-- Building containers', list(packages.keys())) - + status = {} for package in packages: try: - container_name = build_container(name, package, base, build_flags, build_args, simulate, skip_tests, test_only, push) + container_name = build_container(name, package, base, build_flags, build_args, simulate, skip_tests, test_only, push, build_network) except Exception as error: print(error) if not skip_errors: @@ -219,120 +223,120 @@ def build_containers(name, packages, base=get_l4t_base(), build_flags='', build_ status[package] = (container_name, error) else: status[package] = (container_name, None) - + print(f"\n-- Build logs at: {log_dir('build')}") - + for package, (container_name, error) in status.items(): msg = f" * {package} ({container_name}) {'FAILED' if error else 'SUCCESS'}" if error is not None: msg += f" ({error})" print(msg) - + for _, error in status.values(): if error: return False - + return True - - + + def tag_container(source, target, simulate=False): """ Tag a container image (source -> target) """ cmd = f"{sudo_prefix()}docker tag {source} {target}" - + print(f"-- Tagging container {source} -> {target}") print(f"{cmd}\n") - + if not simulate: subprocess.run(cmd, shell=True, check=True) - + def push_container(name, repository='', simulate=False): """ - Push container to a repository or user with 'docker push' + Push container to a repository or user with 'docker push' If repository is specified (for example, a DockerHub username) the container will be re-tagged under that repository first. Otherwise, it's assumed the image is tagged under the correct name already. - + It's also assumed that this machine has already been logged into the repository with 'docker login' - + Returns the container name/tag that was pushed. """ cmd = "" - + if repository: namespace_idx = name.find('/') local_name = name - + if namespace_idx >= 0: name = repository + local_name[namespace_idx:] else: name = repository + '/' + local_name - + print(f"-- Tagging container {local_name} -> {name}") - + cmd += f"{sudo_prefix()}docker rmi {name} ; " cmd += f"{sudo_prefix()}docker tag {local_name} {name} && " - + cmd += f"{sudo_prefix()}docker push {name}" - + print(f"-- Pushing container {name}") print(f"\n{cmd}\n") - + if not simulate: subprocess.run(cmd, executable='/bin/bash', shell=True, check=True) print(f"\n-- Pushed container {name}\n") - + return name - - -def test_container(name, package, simulate=False): + + +def test_container(name, package, simulate=False, test_network='host'): """ Run tests on a container """ package = find_package(package) - + if 'test' not in package: return True - + for test in package['test']: test_cmd = test.split(' ') # test could be a command with arguments test_exe = test_cmd[0] # just get just the script/executable name test_ext = os.path.splitext(test_exe)[1] log_file = os.path.join(log_dir('test'), f"{name.replace('/','_')}_{test_exe}").replace(':','_') - cmd = f"{sudo_prefix()}docker run -t --rm --runtime=nvidia --network=host" + _NEWLINE_ + cmd = f"{sudo_prefix()}docker run -t --rm --runtime=nvidia --network={test_network}" + _NEWLINE_ cmd += f"--volume {package['path']}:/test" + _NEWLINE_ cmd += f"--volume {os.path.join(_PACKAGE_ROOT, 'data')}:/data" + _NEWLINE_ cmd += f"--workdir /test" + _NEWLINE_ cmd += name + _NEWLINE_ - + cmd += "/bin/bash -c '" - + if test_ext == ".py": cmd += f"python3 {test}" elif test_ext == ".sh": cmd += f"/bin/bash {test}" else: cmd += f"{test}" - + cmd += "'" + _NEWLINE_ cmd += f"2>&1 | tee {log_file + '.txt'}" + "; exit ${PIPESTATUS[0]}" - + print(f"-- Testing container {name} ({package['name']}/{test})") print(f"\n{cmd}\n") - + with open(log_file + '.sh', 'w') as cmd_file: cmd_file.write('#!/usr/bin/env bash\n\n') cmd_file.write(cmd + '\n') - - if not simulate: # TODO: return false on errors + + if not simulate: # TODO: return false on errors status = subprocess.run(cmd.replace(_NEWLINE_, ' '), executable='/bin/bash', shell=True, check=True) - + return True - - + + _LOCAL_CACHE=[] _REGISTRY_CACHE=[] @@ -341,34 +345,34 @@ def get_local_containers(): """ Get the locally-available container images from the 'docker images' command Returns a list of dicts with entries like the following: - + {"Containers":"N/A","CreatedAt":"2023-07-23 15:24:28 -0400 EDT","CreatedSince":"42 hours ago", "Digest":"\u003cnone\u003e","ID":"6acd9e526f50","Repository":"runner/l4t-pytorch", - "SharedSize":"N/A","Size":"11.4GB","Tag":"r35.2.1","UniqueSize":"N/A","VirtualSize":"11.37GB"} - + "SharedSize":"N/A","Size":"11.4GB","Tag":"r35.2.1","UniqueSize":"N/A","VirtualSize":"11.37GB"} + These containers are sorted by most recent created to the oldest. """ global _LOCAL_CACHE - + if len(_LOCAL_CACHE) > 0: return _LOCAL_CACHE - + cmd = ["docker", "images", "--format", "'{{json . }}'"] - + if needs_sudo(): cmd = ["sudo"] + cmd - + status = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - #capture_output=True, universal_newlines=True, + #capture_output=True, universal_newlines=True, shell=False, check=True) _LOCAL_CACHE = [json.loads(txt.lstrip("'").rstrip("'")) for txt in status.stdout.decode('ascii').splitlines()] - + pprint_debug(_LOCAL_CACHE) - + return _LOCAL_CACHE - + def get_registry_containers(user='dustynv', use_cache=True, **kwargs): """ @@ -377,19 +381,19 @@ def get_registry_containers(user='dustynv', use_cache=True, **kwargs): To view the number of requests remaining within the rate-limit: curl -i https://hub.docker.com/v2/namespaces/dustynv/repositories/l4t-pytorch/tags - + All the caching is to prevent going over the DockerHub API rate limits. """ global _REGISTRY_CACHE - + if len(_REGISTRY_CACHE) > 0: return _REGISTRY_CACHE - + cache_path = kwargs.get('registry_cache', - os.environ.get('DOCKERHUB_CACHE', + os.environ.get('DOCKERHUB_CACHE', os.path.join(_PACKAGE_ROOT, 'data/containers.json') )) - + has_cache_path = (cache_path != "0" and cache_path.lower() != "off") cache_enabled = (use_cache and has_cache_path) @@ -400,7 +404,7 @@ def get_registry_containers(user='dustynv', use_cache=True, **kwargs): if status.returncode != 0: logging.error(f'failed to update container registry cache from GitHub ({cache_path})') logging.error(f'return code {status.returncode} > {cmd}') - + with open(cache_path) as cache_file: try: _REGISTRY_CACHE = json.load(cache_file) @@ -408,23 +412,23 @@ def get_registry_containers(user='dustynv', use_cache=True, **kwargs): return _REGISTRY_CACHE except Exception: pass - + hub = dockerhub_api.DockerHub(return_lists=True, token=os.environ.get('DOCKERHUB_TOKEN')) _REGISTRY_CACHE = hub.repositories(user) - + for repo in _REGISTRY_CACHE: repo['tags'] = hub.tags(user, repo['name']) if not has_cache_path: cache_path = 'data/containers.json' - + with open(cache_path, 'w') as cache_file: json.dump(_REGISTRY_CACHE, cache_file, indent=2) pprint_debug(_REGISTRY_CACHE) return _REGISTRY_CACHE - - + + def find_local_containers(package, return_dicts=False, **kwargs): """ Search for local containers on the machine containing this package. @@ -433,12 +437,12 @@ def find_local_containers(package, return_dicts=False, **kwargs): """ if isinstance(package, dict): package = package['name'] - + namespace, repo, tag = split_container_name(package) local_images = get_local_containers() - + found_containers = [] - + for image in local_images: if namespace: if image['Repository'] != f'{namespace}/{repo}': @@ -449,62 +453,62 @@ def find_local_containers(package, return_dicts=False, **kwargs): if tag and tag != image['Tag'] and not image['Tag'].startswith(tag + '-'): continue - + if return_dicts: found_containers.append(image) else: found_containers.append(f"{image['Repository']}:{image['Tag']}") - + return found_containers - - + + def find_registry_containers(package, check_l4t_version=True, return_dicts=False, **kwargs): """ Search DockerHub for container images compatible with the package or container name. - - The returned list of images will also be compatible with the version of L4T + + The returned list of images will also be compatible with the version of L4T running on the device, unless check_l4t_version is set to false - + Normally a list of strings is returned, unless return_dicts=True in which case a list of dicts is returned with the full metadata from DockerHub. """ if isinstance(package, dict): package = package['name'] - + namespace, repo, tag = split_container_name(package) registry_repos = get_registry_containers(**kwargs) pprint_debug(registry_repos) found_containers = [] - + for registry_repo in registry_repos: if registry_repo['name'] != repo: continue - + repo_copy = copy.deepcopy(registry_repo) repo_copy['tags'] = [] - + for registry_image in registry_repo['tags']: if tag and not (tag == registry_image['name'] or fnmatch.fnmatch(registry_image['name'], tag + '-*')): continue - + if check_l4t_version: if not l4t_version_compatible(l4t_version_from_tag(registry_image['name']), **kwargs): continue - + repo_copy['tags'].append(copy.deepcopy(registry_image)) - + if not return_dicts: found_containers.append( f"{registry_repo['namespace']}/{registry_repo['name']}:{registry_image['name']}" ) - + if return_dicts and len(repo_copy['tags']) > 0: found_containers.append(repo_copy) - + return found_containers - - + + def find_container(package, prefer_sources=['local', 'registry', 'build'], disable_sources=[], quiet=True, **kwargs): """ Finds a local or remote container image to run for the given package (returns a string) @@ -513,30 +517,30 @@ def find_container(package, prefer_sources=['local', 'registry', 'build'], disab """ if isinstance(package, dict): package = package['name'] - + namespace, repo, tag = split_container_name(package) log_debug(f"-- Finding compatible container image for namespace={namespace} repo={repo} tag={tag}") for source in prefer_sources: if source in disable_sources: continue - + if source == 'local': local_images = find_local_containers(package, **kwargs) - + if len(local_images) > 0: return local_images[0] - + elif source == 'registry': registry_images = find_registry_containers(package, return_dicts=True, **kwargs) - + if len(registry_images) > 0: img = registry_images[0] # TODO allow use to select image if there are multiple candidates img_tag = img['tags'][0] img_name = f"{img['namespace']}/{img['name']}:{img_tag['name']}" if quiet or query_yes_no(f"\nFound compatible container {img_name} ({img_tag['tag_last_pushed'][:10]}, {img_tag['full_size']/(1024**3):.1f}GB) - would you like to pull it?", default="yes"): return img_name - + elif source == 'build': if not quiet and query_yes_no(f"\nCouldn't find a compatible container for {package}, would you like to build it?"): return build_container('', package) #, simulate=True) @@ -579,11 +583,11 @@ def parse_container_versions(tags, use_defaults=True, **kwargs): if not use_defaults: return data - + if not 'L4T_VERSION' in data: print(f"-- Missing L4T_VERSION tag from container '{container}'") return data - + l4t_version = data['L4T_VERSION'] data.setdefault('JETPACK_VERSION', get_jetpack_version(l4t_version=l4t_version)) diff --git a/packages/opencv/test.py b/packages/opencv/test.py index 683063a9b..ce7b1f9ed 100644 --- a/packages/opencv/test.py +++ b/packages/opencv/test.py @@ -4,6 +4,9 @@ import cv2 import sys import requests +import time +from requests.adapters import HTTPAdapter +from urllib3.util.retry import Retry print('OpenCV version:', str(cv2.__version__)) print(cv2.getBuildInformation()) @@ -15,16 +18,48 @@ print('OpenCV was not built with CUDA') raise ex -# download test image +# download test image img_url = 'https://raw.githubusercontent.com/dusty-nv/jetson-containers/59f840abbb99f22914a7b2471da829b3dd56122e/test/data/test_0.jpg' img_path = '/tmp/test_0.jpg' -request = requests.get(img_url, allow_redirects=True) -open(img_path, 'wb').write(request.content) +def download_with_retry(url, dest_path, retries=3, backoff=2): + session = requests.Session() + retry_strategy = Retry( + total=retries, + backoff_factor=backoff, + status_forcelist=[429, 500, 502, 503, 504], + raise_on_status=False + ) + adapter = HTTPAdapter(max_retries=retry_strategy) + session.mount("http://", adapter) + session.mount("https://", adapter) + + try: + print(f'Downloading image from: {url}') + response = session.get(url, timeout=10, allow_redirects=True) + if response.status_code == 200: + with open(dest_path, 'wb') as f: + f.write(response.content) + print(f'Saved image to: {dest_path}') + return True + else: + print(f'❌ Failed to download image. Status code: {response.status_code}') + return False + except requests.RequestException as e: + print(f'❌ Request failed: {e}') + return False + +if not download_with_retry(img_url, img_path): + print('Image download failed. Exiting.') + sys.exit(1) # load image img_cpu = cv2.imread(img_path) -print(f'loaded test image from {img_path} {img_cpu.shape} {img_cpu.dtype}') +if img_cpu is None: + print(f'❌ Failed to load image from {img_path}') + sys.exit(1) + +print(f'✅ Loaded test image from {img_path} shape={img_cpu.shape} dtype={img_cpu.dtype}') # test GPU processing img_gpu = cv2.cuda_GpuMat() @@ -39,4 +74,4 @@ img_gpu = cv2.cuda.createCLAHE(clipLimit=5.0, tileGridSize=(8, 8)).apply(gray, cv2.cuda_Stream.Null()) img_cpu = img_gpu.download() -print('OpenCV OK\n') +print('✅ OpenCV CUDA pipeline executed successfully\n') diff --git a/packages/robots/lerobot/Dockerfile b/packages/robots/lerobot/Dockerfile index f965256c9..67cef5578 100644 --- a/packages/robots/lerobot/Dockerfile +++ b/packages/robots/lerobot/Dockerfile @@ -2,7 +2,7 @@ # name: lerobot # group: robots # docs: docs.md -# depends: [transformers, opencv:4.11.0, pyav, h5py, diffusers, jupyterlab:myst] +# depends: [transformers, opencv:4.11.0, pyav, h5py, diffusers, jupyterlab:myst, numba, mujoco] # requires: '>=36' # test: [test.sh, test.py] #--- @@ -14,17 +14,27 @@ ARG LEROBOT_BRANCH=main RUN git clone --branch=${LEROBOT_BRANCH} --depth=1 https://github.com/${LEROBOT_REPO} /opt/lerobot && \ cd /opt/lerobot && \ + echo "commit: $(git rev-parse HEAD)" > lerobot_version.txt && \ + echo "date: $(git log -1 --format=%cd)" >> lerobot_version.txt && \ + echo "branch: $(git rev-parse --abbrev-ref HEAD)" >> lerobot_version.txt && \ + echo "describe: $(git describe --tags --always)" >> lerobot_version.txt && \ sed -i '/"torch[^"]*",/d' pyproject.toml && \ sed -i '/"opencv-python[^"]*",/d' pyproject.toml && \ sed -i '/"torchvision[^"]*",/d' pyproject.toml && \ sed -i '/"h5py[^"]*",/d' pyproject.toml && \ sed -i '/"av[^"]*",/d' pyproject.toml && \ sed -i '/"huggingface-hub[^"]*",/d' pyproject.toml && \ - sed -i '/"diffusers[^"]*",/d' pyproject.toml && \ + sed -i '/"diffusers[^"]*",/d' pyproject.toml && \ + sed -i '/"rerun-sdk[^"]*",/d' pyproject.toml && \ + sed -i '/"numba[^"]*",/d' pyproject.toml && \ + sed -i '/dependencies = \[/a \ \ \ \ "numpy<2.0.0",' pyproject.toml && \ cat -n pyproject.toml RUN cd /opt/lerobot && \ - pip3 install --ignore-installed --no-deps -e ".[aloha, pusht, dynamixel, pi0, test]" --verbose + pip3 install --no-deps -e ".[aloha, pusht, dynamixel, pi0, test, feetech]" --verbose + +RUN pip install datasets +RUN python3 -c "import datasets; print(datasets.__version__)" RUN apt-get update && \ apt-get install -y --no-install-recommends \ @@ -52,16 +62,19 @@ RUN echo "pactl list short sinks" >> /root/.bash_history RUN echo "pactl set-default-sink 0" >> /root/.bash_history RUN echo "wandb login" >> /root/.bash_history RUN echo "export HF_USER=" >> /root/.bash_history -RUN echo "python lerobot/scripts/control_robot.py record \ - --robot-path lerobot/configs/robot/koch.yaml \ - --fps 30 \ - --root data \ - --repo-id \${HF_USER}/koch_test_\$(date +%Y%m%d_%H%M%S) \ - --tags tutorial \ - --warmup-time-s 5 \ - --episode-time-s 30 \ - --reset-time-s 30 \ - --num-episodes 10" >> /root/.bash_history +RUN echo "export ROBOT_TYPE=so100 #or, koch" >> /root/.bash_history +RUN echo "python lerobot/scripts/control_robot.py \ + --robot.type=\${ROBOT_TYPE} \ + --control.type=record \ + --control.single_task=\"Grasp a lego block and put it in the bin.\" \ + --control.fps 30 \ + --control.repo_id ${HF_USER}/test_$(date +%Y%m%d_%H%M%S) \ + --control.tags '[\"tutorial\"]' \ + --control.warmup_time_s 5 \ + --control.episode_time_s 30 \ + --control.reset_time_s 30 \ + --control.num_episodes 10 \ + --control.push_to_hub=true" >> /root/.bash_history RUN echo -e "* soft core 0\n* hard core 0" >> /etc/security/limits.conf diff --git a/packages/robots/lerobot/clone_lerobot_dir_under_data.sh b/packages/robots/lerobot/clone_lerobot_dir_under_data.sh index ef6365cce..ce795b9c8 100755 --- a/packages/robots/lerobot/clone_lerobot_dir_under_data.sh +++ b/packages/robots/lerobot/clone_lerobot_dir_under_data.sh @@ -18,18 +18,34 @@ REPO_NAME=$(basename -s .git "$REPO_URL") # Full path to the target directory where the repo will be cloned CLONE_PATH="$TARGET_DIR/$REPO_NAME" -# Check if the directory already exists -if [ -d "$CLONE_PATH" ]; then - echo "Directory $CLONE_PATH already exists. Skipping clone." -else - # Clone the repository - git clone "$REPO_URL" "$CLONE_PATH" +# Check for --force option +FORCE=0 +for arg in "$@"; do + if [ "$arg" == "--force" ]; then + FORCE=1 + break + fi +done - # Check if cloning was successful by verifying if the directory exists - if [ ! -d "$CLONE_PATH" ]; then - echo "Error: Failed to clone repository to $CLONE_PATH" - exit 1 +# If the directory exists +if [ -d "$CLONE_PATH" ]; then + if [ "$FORCE" -eq 1 ]; then + echo "Directory $CLONE_PATH already exists. Removing due to --force option." + rm -rf "$CLONE_PATH" else - echo "Repository cloned successfully to $CLONE_PATH." + echo "Directory $CLONE_PATH already exists. Skipping clone. " + echo "You can add '--force' if you want to delete the existing directory and clone from scratch." + exit 0 fi -fi \ No newline at end of file +fi + +# Clone the repository +git clone "$REPO_URL" "$CLONE_PATH" + +# Check if cloning was successful by verifying if the directory exists +if [ ! -d "$CLONE_PATH" ]; then + echo "Error: Failed to clone repository to $CLONE_PATH" + exit 1 +else + echo "Repository cloned successfully to $CLONE_PATH." +fi diff --git a/packages/robots/lerobot/copy_overlay_files_in_data_lerobot.sh b/packages/robots/lerobot/copy_overlay_files_in_data_lerobot.sh index 59308be03..27fd714a9 100755 --- a/packages/robots/lerobot/copy_overlay_files_in_data_lerobot.sh +++ b/packages/robots/lerobot/copy_overlay_files_in_data_lerobot.sh @@ -28,6 +28,6 @@ fi # Copy the contents from the overlay directory to the target directory echo "Copying files from $OVERLAY_DIR to $TARGET_DIR" -cp -r "$OVERLAY_DIR"/* "$TARGET_DIR" +cp -r "$OVERLAY_DIR"/. "$TARGET_DIR" echo "Files copied successfully!" diff --git a/packages/robots/lerobot/lerobot_overlay/lerobot/common/datasets/compute_stats.py b/packages/robots/lerobot/lerobot_overlay/lerobot/common/datasets/compute_stats.py index 4dfb6075c..1149ec83e 100644 --- a/packages/robots/lerobot/lerobot_overlay/lerobot/common/datasets/compute_stats.py +++ b/packages/robots/lerobot/lerobot_overlay/lerobot/common/datasets/compute_stats.py @@ -13,214 +13,164 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from copy import deepcopy -from math import ceil +import numpy as np -import einops -import torch -import tqdm -from datasets import Image +from lerobot.common.datasets.utils import load_image_as_numpy -from lerobot.common.datasets.video_utils import VideoFrame +def estimate_num_samples( + dataset_len: int, min_num_samples: int = 100, max_num_samples: int = 10_000, power: float = 0.75 +) -> int: + """Heuristic to estimate the number of samples based on dataset size. + The power controls the sample growth relative to dataset size. + Lower the power for less number of samples. -def get_stats_einops_patterns(dataset, num_workers=0): - """These einops patterns will be used to aggregate batches and compute statistics. - - Note: We assume the images are in channel first format + For default arguments, we have: + - from 1 to ~500, num_samples=100 + - at 1000, num_samples=177 + - at 2000, num_samples=299 + - at 5000, num_samples=594 + - at 10000, num_samples=1000 + - at 20000, num_samples=1681 """ + if dataset_len < min_num_samples: + min_num_samples = dataset_len + return max(min_num_samples, min(int(dataset_len**power), max_num_samples)) + + +def sample_indices(data_len: int) -> list[int]: + num_samples = estimate_num_samples(data_len) + return np.round(np.linspace(0, data_len - 1, num_samples)).astype(int).tolist() + + +def auto_downsample_height_width(img: np.ndarray, target_size: int = 150, max_size_threshold: int = 300): + _, height, width = img.shape + + if max(width, height) < max_size_threshold: + # no downsampling needed + return img + + downsample_factor = int(width / target_size) if width > height else int(height / target_size) + return img[:, ::downsample_factor, ::downsample_factor] + + +def sample_images(image_paths: list[str]) -> np.ndarray: + sampled_indices = sample_indices(len(image_paths)) + + images = None + for i, idx in enumerate(sampled_indices): + path = image_paths[idx] + # we load as uint8 to reduce memory usage + img = load_image_as_numpy(path, dtype=np.uint8, channel_first=True) + img = auto_downsample_height_width(img) + + if images is None: + images = np.empty((len(sampled_indices), *img.shape), dtype=np.uint8) - dataloader = torch.utils.data.DataLoader( - dataset, - num_workers=num_workers, - batch_size=2, - shuffle=False, - ) - batch = next(iter(dataloader)) - - stats_patterns = {} - for key, feats_type in dataset.features.items(): - # NOTE: skip language_instruction embedding in stats computation - if key == "language_instruction": - continue - - # sanity check that tensors are not float64 - assert batch[key].dtype != torch.float64 - - if isinstance(feats_type, (VideoFrame, Image)): - # sanity check that images are channel first - _, c, h, w = batch[key].shape - assert c < h and c < w, f"expect channel first images, but instead {batch[key].shape}" - - # sanity check that images are float32 in range [0,1] - assert batch[key].dtype == torch.float32, f"expect torch.float32, but instead {batch[key].dtype=}" - assert batch[key].max() <= 1, f"expect pixels lower than 1, but instead {batch[key].max()=}" - assert batch[key].min() >= 0, f"expect pixels greater than 1, but instead {batch[key].min()=}" - - stats_patterns[key] = "b c h w -> c 1 1" - elif batch[key].ndim == 2: - stats_patterns[key] = "b c -> c " - elif batch[key].ndim == 1: - stats_patterns[key] = "b -> 1" + images[i] = img + + return images + + +def get_feature_stats(array: np.ndarray, axis: tuple, keepdims: bool) -> dict[str, np.ndarray]: + return { + "min": np.min(array, axis=axis, keepdims=keepdims), + "max": np.max(array, axis=axis, keepdims=keepdims), + "mean": np.mean(array, axis=axis, keepdims=keepdims), + "std": np.std(array, axis=axis, keepdims=keepdims), + "count": np.array([len(array)]), + } + + +def compute_episode_stats(episode_data: dict[str, list[str] | np.ndarray], features: dict) -> dict: + ep_stats = {} + for key, data in episode_data.items(): + if features[key]["dtype"] == "string": + continue # HACK: we should receive np.arrays of strings + elif features[key]["dtype"] in ["image", "video"]: + ep_ft_array = sample_images(data) # data is a list of image paths + axes_to_reduce = (0, 2, 3) # keep channel dim + keepdims = True else: - raise ValueError(f"{key}, {feats_type}, {batch[key].shape}") - - return stats_patterns - - -def compute_stats(dataset, batch_size=8, num_workers=4, max_num_samples=None, use_gpu=True): - """Compute mean/std and min/max statistics of all data keys in a LeRobotDataset.""" - if max_num_samples is None: - max_num_samples = len(dataset) - - # Check if GPU is available - device = torch.device("cuda" if use_gpu and torch.cuda.is_available() else "cpu") - - # for more info on why we need to set the same number of workers, see `load_from_videos` - stats_patterns = get_stats_einops_patterns(dataset, num_workers) - - - # mean and std will be computed incrementally while max and min will track the running value. - mean, std, max, min = {}, {}, {}, {} - for key in stats_patterns: - mean[key] = torch.tensor(0.0, device=device).float() - std[key] = torch.tensor(0.0, device=device).float() - max[key] = torch.tensor(-float("inf"), device=device).float() - min[key] = torch.tensor(float("inf"), device=device).float() - - def create_seeded_dataloader(dataset, batch_size, seed): - generator = torch.Generator() - generator.manual_seed(seed) - dataloader = torch.utils.data.DataLoader( - dataset, - num_workers=num_workers, - batch_size=batch_size, - shuffle=True, - drop_last=False, - generator=generator, - ) - return dataloader - - # Note: Due to be refactored soon. The point of storing `first_batch` is to make sure we don't get - # surprises when rerunning the sampler. - first_batch = None - running_item_count = 0 # for online mean computation - dataloader = create_seeded_dataloader(dataset, batch_size, seed=1337) - for i, batch in enumerate( - tqdm.tqdm(dataloader, total=ceil(max_num_samples / batch_size), desc="Compute mean, min, max") - ): - this_batch_size = len(batch["index"]) - running_item_count += this_batch_size - - # Move batch to device - for key in stats_patterns: - batch[key] = batch[key].float().to(device) - - if first_batch is None: - first_batch = deepcopy(batch) - - for key, pattern in stats_patterns.items(): - # Numerically stable update step for mean computation. - batch_mean = einops.reduce(batch[key], pattern, "mean") - # Hint: to update the mean we need x̄ₙ = (Nₙ₋₁x̄ₙ₋₁ + Bₙxₙ) / Nₙ, where the subscript represents - # the update step, N is the running item count, B is this batch size, x̄ is the running mean, - # and x is the current batch mean. Some rearrangement is then required to avoid risking - # numerical overflow. Another hint: Nₙ₋₁ = Nₙ - Bₙ. Rearrangement yields - # x̄ₙ = x̄ₙ₋₁ + Bₙ * (xₙ - x̄ₙ₋₁) / Nₙ - mean[key] = mean[key] + this_batch_size * (batch_mean - mean[key]) / running_item_count - max[key] = torch.maximum(max[key], einops.reduce(batch[key], pattern, "max")) - min[key] = torch.minimum(min[key], einops.reduce(batch[key], pattern, "min")) - - if i == ceil(max_num_samples / batch_size) - 1: - break - - first_batch_ = None - running_item_count = 0 # for online std computation - dataloader = create_seeded_dataloader(dataset, batch_size, seed=1337) - for i, batch in enumerate( - tqdm.tqdm(dataloader, total=ceil(max_num_samples / batch_size), desc="Compute std") - ): - this_batch_size = len(batch["index"]) - running_item_count += this_batch_size - - for key in stats_patterns: - batch[key] = batch[key].float().to(device) - - # Sanity check to make sure the batches are still in the same order as before. - if first_batch_ is None: - first_batch_ = deepcopy(batch) - for key in stats_patterns: - assert torch.equal(first_batch_[key], first_batch[key]) - - for key, pattern in stats_patterns.items(): - # Numerically stable update step for mean computation (where the mean is over squared - # residuals).See notes in the mean computation loop above. - batch_std = einops.reduce((batch[key] - mean[key]) ** 2, pattern, "mean").to(device) - std[key] = std[key] + this_batch_size * (batch_std - std[key]) / running_item_count - - if i == ceil(max_num_samples / batch_size) - 1: - break - - for key in stats_patterns: - std[key] = torch.sqrt(std[key]) - - stats = {} - for key in stats_patterns: - stats[key] = { - "mean": mean[key], - "std": std[key], - "max": max[key], - "min": min[key], - } - return stats - - -def aggregate_stats(ls_datasets) -> dict[str, torch.Tensor]: - """Aggregate stats of multiple LeRobot datasets into one set of stats without recomputing from scratch. - - The final stats will have the union of all data keys from each of the datasets. - - The final stats will have the union of all data keys from each of the datasets. For instance: - - new_max = max(max_dataset_0, max_dataset_1, ...) + ep_ft_array = data # data is already a np.ndarray + axes_to_reduce = 0 # compute stats over the first axis + keepdims = data.ndim == 1 # keep as np.array + + ep_stats[key] = get_feature_stats(ep_ft_array, axis=axes_to_reduce, keepdims=keepdims) + + # finally, we normalize and remove batch dim for images + if features[key]["dtype"] in ["image", "video"]: + ep_stats[key] = { + k: v if k == "count" else np.squeeze(v / 255.0, axis=0) for k, v in ep_stats[key].items() + } + + return ep_stats + + +def _assert_type_and_shape(stats_list: list[dict[str, dict]]): + for i in range(len(stats_list)): + for fkey in stats_list[i]: + for k, v in stats_list[i][fkey].items(): + if not isinstance(v, np.ndarray): + raise ValueError( + f"Stats must be composed of numpy array, but key '{k}' of feature '{fkey}' is of type '{type(v)}' instead." + ) + if v.ndim == 0: + raise ValueError("Number of dimensions must be at least 1, and is 0 instead.") + if k == "count" and v.shape != (1,): + raise ValueError(f"Shape of 'count' must be (1), but is {v.shape} instead.") + if "image" in fkey and k != "count" and v.shape != (3, 1, 1): + raise ValueError(f"Shape of '{k}' must be (3,1,1), but is {v.shape} instead.") + + +def aggregate_feature_stats(stats_ft_list: list[dict[str, dict]]) -> dict[str, dict[str, np.ndarray]]: + """Aggregates stats for a single feature.""" + means = np.stack([s["mean"] for s in stats_ft_list]) + variances = np.stack([s["std"] ** 2 for s in stats_ft_list]) + counts = np.stack([s["count"] for s in stats_ft_list]) + total_count = counts.sum(axis=0) + + # Prepare weighted mean by matching number of dimensions + while counts.ndim < means.ndim: + counts = np.expand_dims(counts, axis=-1) + + # Compute the weighted mean + weighted_means = means * counts + total_mean = weighted_means.sum(axis=0) / total_count + + # Compute the variance using the parallel algorithm + delta_means = means - total_mean + weighted_variances = (variances + delta_means**2) * counts + total_variance = weighted_variances.sum(axis=0) / total_count + + return { + "min": np.min(np.stack([s["min"] for s in stats_ft_list]), axis=0), + "max": np.max(np.stack([s["max"] for s in stats_ft_list]), axis=0), + "mean": total_mean, + "std": np.sqrt(total_variance), + "count": total_count, + } + + +def aggregate_stats(stats_list: list[dict[str, dict]]) -> dict[str, dict[str, np.ndarray]]: + """Aggregate stats from multiple compute_stats outputs into a single set of stats. + + The final stats will have the union of all data keys from each of the stats dicts. + + For instance: - new_min = min(min_dataset_0, min_dataset_1, ...) - - new_mean = (mean of all data) + - new_max = max(max_dataset_0, max_dataset_1, ...) + - new_mean = (mean of all data, weighted by counts) - new_std = (std of all data) """ - data_keys = set() - for dataset in ls_datasets: - data_keys.update(dataset.stats.keys()) - stats = {k: {} for k in data_keys} - for data_key in data_keys: - for stat_key in ["min", "max"]: - # compute `max(dataset_0["max"], dataset_1["max"], ...)` - stats[data_key][stat_key] = einops.reduce( - torch.stack([d.stats[data_key][stat_key] for d in ls_datasets if data_key in d.stats], dim=0), - "n ... -> ...", - stat_key, - ) - total_samples = sum(d.num_samples for d in ls_datasets if data_key in d.stats) - # Compute the "sum" statistic by multiplying each mean by the number of samples in the respective - # dataset, then divide by total_samples to get the overall "mean". - # NOTE: the brackets around (d.num_samples / total_samples) are needed tor minimize the risk of - # numerical overflow! - stats[data_key]["mean"] = sum( - d.stats[data_key]["mean"] * (d.num_samples / total_samples) - for d in ls_datasets - if data_key in d.stats - ) - # The derivation for standard deviation is a little more involved but is much in the same spirit as - # the computation of the mean. - # Given two sets of data where the statistics are known: - # σ_combined = sqrt[ (n1 * (σ1^2 + d1^2) + n2 * (σ2^2 + d2^2)) / (n1 + n2) ] - # where d1 = μ1 - μ_combined, d2 = μ2 - μ_combined - # NOTE: the brackets around (d.num_samples / total_samples) are needed tor minimize the risk of - # numerical overflow! - stats[data_key]["std"] = torch.sqrt( - sum( - (d.stats[data_key]["std"] ** 2 + (d.stats[data_key]["mean"] - stats[data_key]["mean"]) ** 2) - * (d.num_samples / total_samples) - for d in ls_datasets - if data_key in d.stats - ) - ) - return stats + + _assert_type_and_shape(stats_list) + + data_keys = {key for stats in stats_list for key in stats} + aggregated_stats = {key: {} for key in data_keys} + + for key in data_keys: + stats_with_key = [stats[key] for stats in stats_list if key in stats] + aggregated_stats[key] = aggregate_feature_stats(stats_with_key) + + return aggregated_stats diff --git a/packages/robots/lerobot/lerobot_overlay/lerobot/common/robot_devices/robots/manipulator.py b/packages/robots/lerobot/lerobot_overlay/lerobot/common/robot_devices/robots/manipulator.py new file mode 100644 index 000000000..d00b6b7a4 --- /dev/null +++ b/packages/robots/lerobot/lerobot_overlay/lerobot/common/robot_devices/robots/manipulator.py @@ -0,0 +1,637 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Contains logic to instantiate a robot, read information from its motors and cameras, +and send orders to its motors. +""" +# TODO(rcadene, aliberts): reorganize the codebase into one file per robot, with the associated +# calibration procedure, to make it easy for people to add their own robot. + +import json +import logging +import time +import warnings +from pathlib import Path + +import numpy as np +import torch + +from lerobot.common.robot_devices.cameras.utils import make_cameras_from_configs +from lerobot.common.robot_devices.motors.utils import MotorsBus, make_motors_buses_from_configs +from lerobot.common.robot_devices.robots.configs import ManipulatorRobotConfig +from lerobot.common.robot_devices.robots.utils import get_arm_id +from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError + +def safe_tensor(data): + try: + return torch.from_numpy(np.array(data, dtype=np.float32, copy=True)) + except TypeError: + # Ensure tensor is at least 1D + return torch.tensor(data, dtype=torch.float32).reshape(-1) + +def ensure_safe_goal_position( + goal_pos: torch.Tensor, present_pos: torch.Tensor, max_relative_target: float | list[float] +): + # Cap relative action target magnitude for safety. + diff = goal_pos - present_pos + max_relative_target = torch.tensor(max_relative_target) + safe_diff = torch.minimum(diff, max_relative_target) + safe_diff = torch.maximum(safe_diff, -max_relative_target) + safe_goal_pos = present_pos + safe_diff + + if not torch.allclose(goal_pos, safe_goal_pos): + logging.warning( + "Relative goal position magnitude had to be clamped to be safe.\n" + f" requested relative goal position target: {diff}\n" + f" clamped relative goal position target: {safe_diff}" + ) + + return safe_goal_pos + + +class ManipulatorRobot: + # TODO(rcadene): Implement force feedback + """This class allows to control any manipulator robot of various number of motors. + + Non exhaustive list of robots: + - [Koch v1.0](https://github.com/AlexanderKoch-Koch/low_cost_robot), with and without the wrist-to-elbow expansion, developed + by Alexander Koch from [Tau Robotics](https://tau-robotics.com) + - [Koch v1.1](https://github.com/jess-moss/koch-v1-1) developed by Jess Moss + - [Aloha](https://www.trossenrobotics.com/aloha-kits) developed by Trossen Robotics + + Example of instantiation, a pre-defined robot config is required: + ```python + robot = ManipulatorRobot(KochRobotConfig()) + ``` + + Example of overwriting motors during instantiation: + ```python + # Defines how to communicate with the motors of the leader and follower arms + leader_arms = { + "main": DynamixelMotorsBusConfig( + port="/dev/tty.usbmodem575E0031751", + motors={ + # name: (index, model) + "shoulder_pan": (1, "xl330-m077"), + "shoulder_lift": (2, "xl330-m077"), + "elbow_flex": (3, "xl330-m077"), + "wrist_flex": (4, "xl330-m077"), + "wrist_roll": (5, "xl330-m077"), + "gripper": (6, "xl330-m077"), + }, + ), + } + follower_arms = { + "main": DynamixelMotorsBusConfig( + port="/dev/tty.usbmodem575E0032081", + motors={ + # name: (index, model) + "shoulder_pan": (1, "xl430-w250"), + "shoulder_lift": (2, "xl430-w250"), + "elbow_flex": (3, "xl330-m288"), + "wrist_flex": (4, "xl330-m288"), + "wrist_roll": (5, "xl330-m288"), + "gripper": (6, "xl330-m288"), + }, + ), + } + robot_config = KochRobotConfig(leader_arms=leader_arms, follower_arms=follower_arms) + robot = ManipulatorRobot(robot_config) + ``` + + Example of overwriting cameras during instantiation: + ```python + # Defines how to communicate with 2 cameras connected to the computer. + # Here, the webcam of the laptop and the phone (connected in USB to the laptop) + # can be reached respectively using the camera indices 0 and 1. These indices can be + # arbitrary. See the documentation of `OpenCVCamera` to find your own camera indices. + cameras = { + "laptop": OpenCVCamera(camera_index=0, fps=30, width=640, height=480), + "phone": OpenCVCamera(camera_index=1, fps=30, width=640, height=480), + } + robot = ManipulatorRobot(KochRobotConfig(cameras=cameras)) + ``` + + Once the robot is instantiated, connect motors buses and cameras if any (Required): + ```python + robot.connect() + ``` + + Example of highest frequency teleoperation, which doesn't require cameras: + ```python + while True: + robot.teleop_step() + ``` + + Example of highest frequency data collection from motors and cameras (if any): + ```python + while True: + observation, action = robot.teleop_step(record_data=True) + ``` + + Example of controlling the robot with a policy: + ```python + while True: + # Uses the follower arms and cameras to capture an observation + observation = robot.capture_observation() + + # Assumes a policy has been instantiated + with torch.inference_mode(): + action = policy.select_action(observation) + + # Orders the robot to move + robot.send_action(action) + ``` + + Example of disconnecting which is not mandatory since we disconnect when the object is deleted: + ```python + robot.disconnect() + ``` + """ + + def __init__( + self, + config: ManipulatorRobotConfig, + ): + self.config = config + self.robot_type = self.config.type + self.calibration_dir = Path(self.config.calibration_dir) + self.leader_arms = make_motors_buses_from_configs(self.config.leader_arms) + self.follower_arms = make_motors_buses_from_configs(self.config.follower_arms) + self.cameras = make_cameras_from_configs(self.config.cameras) + self.is_connected = False + self.logs = {} + + def get_motor_names(self, arm: dict[str, MotorsBus]) -> list: + return [f"{arm}_{motor}" for arm, bus in arm.items() for motor in bus.motors] + + @property + def camera_features(self) -> dict: + cam_ft = {} + for cam_key, cam in self.cameras.items(): + key = f"observation.images.{cam_key}" + cam_ft[key] = { + "shape": (cam.height, cam.width, cam.channels), + "names": ["height", "width", "channels"], + "info": None, + } + return cam_ft + + @property + def motor_features(self) -> dict: + action_names = self.get_motor_names(self.leader_arms) + state_names = self.get_motor_names(self.leader_arms) + return { + "action": { + "dtype": "float32", + "shape": (len(action_names),), + "names": action_names, + }, + "observation.state": { + "dtype": "float32", + "shape": (len(state_names),), + "names": state_names, + }, + } + + @property + def features(self): + return {**self.motor_features, **self.camera_features} + + @property + def has_camera(self): + return len(self.cameras) > 0 + + @property + def num_cameras(self): + return len(self.cameras) + + @property + def available_arms(self): + available_arms = [] + for name in self.follower_arms: + arm_id = get_arm_id(name, "follower") + available_arms.append(arm_id) + for name in self.leader_arms: + arm_id = get_arm_id(name, "leader") + available_arms.append(arm_id) + return available_arms + + def connect(self): + if self.is_connected: + raise RobotDeviceAlreadyConnectedError( + "ManipulatorRobot is already connected. Do not run `robot.connect()` twice." + ) + + if not self.leader_arms and not self.follower_arms and not self.cameras: + raise ValueError( + "ManipulatorRobot doesn't have any device to connect. See example of usage in docstring of the class." + ) + + # Connect the arms + for name in self.follower_arms: + print(f"Connecting {name} follower arm.") + self.follower_arms[name].connect() + for name in self.leader_arms: + print(f"Connecting {name} leader arm.") + self.leader_arms[name].connect() + + if self.robot_type in ["koch", "koch_bimanual", "aloha"]: + from lerobot.common.robot_devices.motors.dynamixel import TorqueMode + elif self.robot_type in ["so100", "moss", "lekiwi"]: + from lerobot.common.robot_devices.motors.feetech import TorqueMode + + # We assume that at connection time, arms are in a rest position, and torque can + # be safely disabled to run calibration and/or set robot preset configurations. + for name in self.follower_arms: + self.follower_arms[name].write("Torque_Enable", TorqueMode.DISABLED.value) + for name in self.leader_arms: + self.leader_arms[name].write("Torque_Enable", TorqueMode.DISABLED.value) + + self.activate_calibration() + + # Set robot preset (e.g. torque in leader gripper for Koch v1.1) + if self.robot_type in ["koch", "koch_bimanual"]: + self.set_koch_robot_preset() + elif self.robot_type == "aloha": + self.set_aloha_robot_preset() + elif self.robot_type in ["so100", "moss", "lekiwi"]: + self.set_so100_robot_preset() + + # Enable torque on all motors of the follower arms + for name in self.follower_arms: + print(f"Activating torque on {name} follower arm.") + self.follower_arms[name].write("Torque_Enable", 1) + + if self.config.gripper_open_degree is not None: + if self.robot_type not in ["koch", "koch_bimanual"]: + raise NotImplementedError( + f"{self.robot_type} does not support position AND current control in the handle, which is require to set the gripper open." + ) + # Set the leader arm in torque mode with the gripper motor set to an angle. This makes it possible + # to squeeze the gripper and have it spring back to an open position on its own. + for name in self.leader_arms: + self.leader_arms[name].write("Torque_Enable", 1, "gripper") + self.leader_arms[name].write("Goal_Position", self.config.gripper_open_degree, "gripper") + + # Check both arms can be read + for name in self.follower_arms: + self.follower_arms[name].read("Present_Position") + for name in self.leader_arms: + self.leader_arms[name].read("Present_Position") + + # Connect the cameras + for name in self.cameras: + self.cameras[name].connect() + + self.is_connected = True + + def activate_calibration(self): + """After calibration all motors function in human interpretable ranges. + Rotations are expressed in degrees in nominal range of [-180, 180], + and linear motions (like gripper of Aloha) in nominal range of [0, 100]. + """ + + def load_or_run_calibration_(name, arm, arm_type): + arm_id = get_arm_id(name, arm_type) + arm_calib_path = self.calibration_dir / f"{arm_id}.json" + + if arm_calib_path.exists(): + with open(arm_calib_path) as f: + calibration = json.load(f) + else: + # TODO(rcadene): display a warning in __init__ if calibration file not available + print(f"Missing calibration file '{arm_calib_path}'") + + if self.robot_type in ["koch", "koch_bimanual", "aloha"]: + from lerobot.common.robot_devices.robots.dynamixel_calibration import run_arm_calibration + + calibration = run_arm_calibration(arm, self.robot_type, name, arm_type) + + elif self.robot_type in ["so100", "moss", "lekiwi"]: + from lerobot.common.robot_devices.robots.feetech_calibration import ( + run_arm_manual_calibration, + ) + + calibration = run_arm_manual_calibration(arm, self.robot_type, name, arm_type) + + print(f"Calibration is done! Saving calibration file '{arm_calib_path}'") + arm_calib_path.parent.mkdir(parents=True, exist_ok=True) + with open(arm_calib_path, "w") as f: + json.dump(calibration, f) + + return calibration + + for name, arm in self.follower_arms.items(): + calibration = load_or_run_calibration_(name, arm, "follower") + arm.set_calibration(calibration) + for name, arm in self.leader_arms.items(): + calibration = load_or_run_calibration_(name, arm, "leader") + arm.set_calibration(calibration) + + def set_koch_robot_preset(self): + def set_operating_mode_(arm): + from lerobot.common.robot_devices.motors.dynamixel import TorqueMode + + if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any(): + raise ValueError("To run set robot preset, the torque must be disabled on all motors.") + + # Use 'extended position mode' for all motors except gripper, because in joint mode the servos can't + # rotate more than 360 degrees (from 0 to 4095) And some mistake can happen while assembling the arm, + # you could end up with a servo with a position 0 or 4095 at a crucial point See [ + # https://emanual.robotis.com/docs/en/dxl/x/x_series/#operating-mode11] + all_motors_except_gripper = [name for name in arm.motor_names if name != "gripper"] + if len(all_motors_except_gripper) > 0: + # 4 corresponds to Extended Position on Koch motors + arm.write("Operating_Mode", 4, all_motors_except_gripper) + + # Use 'position control current based' for gripper to be limited by the limit of the current. + # For the follower gripper, it means it can grasp an object without forcing too much even tho, + # it's goal position is a complete grasp (both gripper fingers are ordered to join and reach a touch). + # For the leader gripper, it means we can use it as a physical trigger, since we can force with our finger + # to make it move, and it will move back to its original target position when we release the force. + # 5 corresponds to Current Controlled Position on Koch gripper motors "xl330-m077, xl330-m288" + arm.write("Operating_Mode", 5, "gripper") + + for name in self.follower_arms: + set_operating_mode_(self.follower_arms[name]) + + # Set better PID values to close the gap between recorded states and actions + # TODO(rcadene): Implement an automatic procedure to set optimal PID values for each motor + self.follower_arms[name].write("Position_P_Gain", 1500, "elbow_flex") + self.follower_arms[name].write("Position_I_Gain", 0, "elbow_flex") + self.follower_arms[name].write("Position_D_Gain", 600, "elbow_flex") + + if self.config.gripper_open_degree is not None: + for name in self.leader_arms: + set_operating_mode_(self.leader_arms[name]) + + # Enable torque on the gripper of the leader arms, and move it to 45 degrees, + # so that we can use it as a trigger to close the gripper of the follower arms. + self.leader_arms[name].write("Torque_Enable", 1, "gripper") + self.leader_arms[name].write("Goal_Position", self.config.gripper_open_degree, "gripper") + + def set_aloha_robot_preset(self): + def set_shadow_(arm): + # Set secondary/shadow ID for shoulder and elbow. These joints have two motors. + # As a result, if only one of them is required to move to a certain position, + # the other will follow. This is to avoid breaking the motors. + if "shoulder_shadow" in arm.motor_names: + shoulder_idx = arm.read("ID", "shoulder") + arm.write("Secondary_ID", shoulder_idx, "shoulder_shadow") + + if "elbow_shadow" in arm.motor_names: + elbow_idx = arm.read("ID", "elbow") + arm.write("Secondary_ID", elbow_idx, "elbow_shadow") + + for name in self.follower_arms: + set_shadow_(self.follower_arms[name]) + + for name in self.leader_arms: + set_shadow_(self.leader_arms[name]) + + for name in self.follower_arms: + # Set a velocity limit of 131 as advised by Trossen Robotics + self.follower_arms[name].write("Velocity_Limit", 131) + + # Use 'extended position mode' for all motors except gripper, because in joint mode the servos can't + # rotate more than 360 degrees (from 0 to 4095) And some mistake can happen while assembling the arm, + # you could end up with a servo with a position 0 or 4095 at a crucial point See [ + # https://emanual.robotis.com/docs/en/dxl/x/x_series/#operating-mode11] + all_motors_except_gripper = [ + name for name in self.follower_arms[name].motor_names if name != "gripper" + ] + if len(all_motors_except_gripper) > 0: + # 4 corresponds to Extended Position on Aloha motors + self.follower_arms[name].write("Operating_Mode", 4, all_motors_except_gripper) + + # Use 'position control current based' for follower gripper to be limited by the limit of the current. + # It can grasp an object without forcing too much even tho, + # it's goal position is a complete grasp (both gripper fingers are ordered to join and reach a touch). + # 5 corresponds to Current Controlled Position on Aloha gripper follower "xm430-w350" + self.follower_arms[name].write("Operating_Mode", 5, "gripper") + + # Note: We can't enable torque on the leader gripper since "xc430-w150" doesn't have + # a Current Controlled Position mode. + + if self.config.gripper_open_degree is not None: + warnings.warn( + f"`gripper_open_degree` is set to {self.config.gripper_open_degree}, but None is expected for Aloha instead", + stacklevel=1, + ) + + def set_so100_robot_preset(self): + for name in self.follower_arms: + # Mode=0 for Position Control + self.follower_arms[name].write("Mode", 0) + # Set P_Coefficient to lower value to avoid shakiness (Default is 32) + self.follower_arms[name].write("P_Coefficient", 16) + # Set I_Coefficient and D_Coefficient to default value 0 and 32 + self.follower_arms[name].write("I_Coefficient", 0) + self.follower_arms[name].write("D_Coefficient", 32) + # Close the write lock so that Maximum_Acceleration gets written to EPROM address, + # which is mandatory for Maximum_Acceleration to take effect after rebooting. + self.follower_arms[name].write("Lock", 0) + # Set Maximum_Acceleration to 254 to speedup acceleration and deceleration of + # the motors. Note: this configuration is not in the official STS3215 Memory Table + self.follower_arms[name].write("Maximum_Acceleration", 254) + self.follower_arms[name].write("Acceleration", 254) + + def teleop_step( + self, record_data=False + ) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]: + if not self.is_connected: + raise RobotDeviceNotConnectedError( + "ManipulatorRobot is not connected. You need to run `robot.connect()`." + ) + + # Prepare to assign the position of the leader to the follower + leader_pos = {} + for name in self.leader_arms: + before_lread_t = time.perf_counter() + leader_pos[name] = self.leader_arms[name].read("Present_Position") + leader_pos[name] = safe_tensor(leader_pos[name]) ############################################ + self.logs[f"read_leader_{name}_pos_dt_s"] = time.perf_counter() - before_lread_t + + # Send goal position to the follower + follower_goal_pos = {} + for name in self.follower_arms: + before_fwrite_t = time.perf_counter() + goal_pos = leader_pos[name] + + # Cap goal position when too far away from present position. + # Slower fps expected due to reading from the follower. + if self.config.max_relative_target is not None: + present_pos = self.follower_arms[name].read("Present_Position") + present_pos = torch.from_numpy(present_pos) + goal_pos = ensure_safe_goal_position(goal_pos, present_pos, self.config.max_relative_target) + + # Used when record_data=True + follower_goal_pos[name] = goal_pos + + goal_pos_cpu = goal_pos.to('cpu') # ensure on CPU + goal_np = goal_pos_cpu.numpy() # now safely get NumPy array + + goal_pos = np.array(goal_np, dtype=np.float32, copy=True) + + self.follower_arms[name].write("Goal_Position", goal_pos) + self.logs[f"write_follower_{name}_goal_pos_dt_s"] = time.perf_counter() - before_fwrite_t + + # Early exit when recording data is not requested + if not record_data: + return + + # TODO(rcadene): Add velocity and other info + # Read follower position + follower_pos = {} + for name in self.follower_arms: + before_fread_t = time.perf_counter() + follower_pos[name] = self.follower_arms[name].read("Present_Position") + follower_pos[name] = safe_tensor(follower_pos[name]) + self.logs[f"read_follower_{name}_pos_dt_s"] = time.perf_counter() - before_fread_t + + # Create state by concatenating follower current position + state = [] + for name in self.follower_arms: + if name in follower_pos: + state.append(follower_pos[name]) + state = torch.cat(state) + + # Create action by concatenating follower goal position + action = [] + for name in self.follower_arms: + if name in follower_goal_pos: + action.append(follower_goal_pos[name]) + action = torch.cat(action) + + # Capture images from cameras + images = {} + for name in self.cameras: + before_camread_t = time.perf_counter() + images[name] = self.cameras[name].async_read() + images[name] = torch.from_numpy(images[name]) + self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"] + self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t + + # Populate output dictionaries + obs_dict, action_dict = {}, {} + obs_dict["observation.state"] = state + action_dict["action"] = action + for name in self.cameras: + obs_dict[f"observation.images.{name}"] = images[name] + + return obs_dict, action_dict + + def capture_observation(self): + """The returned observations do not have a batch dimension.""" + if not self.is_connected: + raise RobotDeviceNotConnectedError( + "ManipulatorRobot is not connected. You need to run `robot.connect()`." + ) + + # Read follower position + follower_pos = {} + for name in self.follower_arms: + before_fread_t = time.perf_counter() + follower_pos[name] = self.follower_arms[name].read("Present_Position") + follower_pos[name] = torch.from_numpy(follower_pos[name]) + self.logs[f"read_follower_{name}_pos_dt_s"] = time.perf_counter() - before_fread_t + + # Create state by concatenating follower current position + state = [] + for name in self.follower_arms: + if name in follower_pos: + state.append(follower_pos[name]) + state = torch.cat(state) + + # Capture images from cameras + images = {} + for name in self.cameras: + before_camread_t = time.perf_counter() + images[name] = self.cameras[name].async_read() + images[name] = torch.from_numpy(images[name]) + self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"] + self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t + + # Populate output dictionaries and format to pytorch + obs_dict = {} + obs_dict["observation.state"] = state + for name in self.cameras: + obs_dict[f"observation.images.{name}"] = images[name] + return obs_dict + + def send_action(self, action: torch.Tensor) -> torch.Tensor: + """Command the follower arms to move to a target joint configuration. + + The relative action magnitude may be clipped depending on the configuration parameter + `max_relative_target`. In this case, the action sent differs from original action. + Thus, this function always returns the action actually sent. + + Args: + action: tensor containing the concatenated goal positions for the follower arms. + """ + if not self.is_connected: + raise RobotDeviceNotConnectedError( + "ManipulatorRobot is not connected. You need to run `robot.connect()`." + ) + + from_idx = 0 + to_idx = 0 + action_sent = [] + for name in self.follower_arms: + # Get goal position of each follower arm by splitting the action vector + to_idx += len(self.follower_arms[name].motor_names) + goal_pos = action[from_idx:to_idx] + from_idx = to_idx + + # Cap goal position when too far away from present position. + # Slower fps expected due to reading from the follower. + if self.config.max_relative_target is not None: + present_pos = self.follower_arms[name].read("Present_Position") + present_pos = torch.from_numpy(present_pos) + goal_pos = ensure_safe_goal_position(goal_pos, present_pos, self.config.max_relative_target) + + # Save tensor to concat and return + action_sent.append(goal_pos) + + # Send goal position to each follower + goal_pos = goal_pos.numpy().astype(np.float32) + self.follower_arms[name].write("Goal_Position", goal_pos) + + return torch.cat(action_sent) + + def print_logs(self): + pass + # TODO(aliberts): move robot-specific logs logic here + + def disconnect(self): + if not self.is_connected: + raise RobotDeviceNotConnectedError( + "ManipulatorRobot is not connected. You need to run `robot.connect()` before disconnecting." + ) + + for name in self.follower_arms: + self.follower_arms[name].disconnect() + + for name in self.leader_arms: + self.leader_arms[name].disconnect() + + for name in self.cameras: + self.cameras[name].disconnect() + + self.is_connected = False + + def __del__(self): + if getattr(self, "is_connected", False): + self.disconnect() diff --git a/packages/robots/lerobot/lerobot_overlay/notebooks/7-2_real-robot_configure-motors.ipynb b/packages/robots/lerobot/lerobot_overlay/notebooks/7-2_real-robot_configure-motors.ipynb index 75bfd4d91..09327e0c5 100644 --- a/packages/robots/lerobot/lerobot_overlay/notebooks/7-2_real-robot_configure-motors.ipynb +++ b/packages/robots/lerobot/lerobot_overlay/notebooks/7-2_real-robot_configure-motors.ipynb @@ -24,7 +24,7 @@ "source": [ "Skip the initial section of the original document, where it talks about the installation of dependencies, connecting 5V and 12V powers and connectiong USB cables.\n", "\n", - "Instead, skip to \"Instatntiate the DynamixelMotorBus\" section of this notebook." + "Instead, skip to \"Instatntiate the DynamixelMotorsBus\" section of this notebook." ] }, { @@ -139,21 +139,21 @@ "You can perform this with GUI on your daily PC, be that Mac, Windows or Linux.
\n", "Below image shows what you should get at the end of following the path of updating firmware and assinging IDs to each motors on Dynamixel Wizard 2.0 on Windows. \n", "\n", - "![image.png](attachment:image.png)\n", + "![image.png](attachment:image.png){ witdh=\"640\" }\n", "\n", "Once everything is set up, run the following cells (inside the lerobot container running on Jetson) to check if everything is set up correctly." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/dev/ttyACM0 /dev/ttyACM1 /dev/ttyACM_kochfollower /dev/ttyACM_kochleader\n" + "/dev/ttyACM0 /dev/ttyACM2 /dev/ttyACM_kochfollower /dev/ttyACM_kochleader\n" ] } ], @@ -170,16 +170,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ + "from lerobot.common.robot_devices.motors.configs import DynamixelMotorsBusConfig\n", "from lerobot.common.robot_devices.motors.dynamixel import DynamixelMotorsBus\n", "\n", "leader_port = \"/dev/ttyACM_kochleader\"\n", "follower_port = \"/dev/ttyACM_kochfollower\"\n", "\n", - "leader_arm = DynamixelMotorsBus(\n", + "leader_config = DynamixelMotorsBusConfig(\n", " port=leader_port,\n", " motors={\n", " # name: (index, model)\n", @@ -192,7 +193,7 @@ " },\n", ")\n", "\n", - "follower_arm = DynamixelMotorsBus(\n", + "follower_config = DynamixelMotorsBusConfig(\n", " port=follower_port,\n", " motors={\n", " # name: (index, model)\n", @@ -204,18 +205,70 @@ " \"wrist_roll\": (5, \"xl330-m288\"),\n", " \"gripper\": (6, \"xl330-m288\"),\n", " },\n", - ")" + ")\n", + "\n", + "leader_arm = DynamixelMotorsBus(leader_config)\n", + "follower_arm = DynamixelMotorsBus(follower_config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Updating the YAML Configuration File\n", - "\n", - "Next, open [`./lerobot/configs/robot/koch.yaml`](../lerobot/configs/robot/koch.yaml) and check it has the correct port names you see on the system.\n", + "#### Updating `KochRobotConfig`\n", + "\n", + "Next, open [`../lerobot/common/robot_devices/robots/configs.py`](../lerobot/common/robot_devices/robots/configs.py) and updates the ports.\n", + "\n", + "It should look something like:\n", + "\n", + ":::{dropdown} configs.py example (click to open)\n", + "\n", + "```python\n", + "@RobotConfig.register_subclass(\"koch\")\n", + "@dataclass\n", + "class KochRobotConfig(ManipulatorRobotConfig):\n", + " calibration_dir: str = \".cache/calibration/koch\"\n", + " # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.\n", + " # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as\n", + " # the number of motors in your follower arms.\n", + " max_relative_target: int | None = None\n", + "\n", + " leader_arms: dict[str, MotorsBusConfig] = field(\n", + " default_factory=lambda: {\n", + " \"main\": DynamixelMotorsBusConfig(\n", + " port=\"/dev/ttyACM_kochleader\", <-- UPDATE HERE\n", + " motors={\n", + " # name: (index, model)\n", + " \"shoulder_pan\": [1, \"xl330-m077\"],\n", + " \"shoulder_lift\": [2, \"xl330-m077\"],\n", + " \"elbow_flex\": [3, \"xl330-m077\"],\n", + " \"wrist_flex\": [4, \"xl330-m077\"],\n", + " \"wrist_roll\": [5, \"xl330-m077\"],\n", + " \"gripper\": [6, \"xl330-m077\"],\n", + " },\n", + " ),\n", + " }\n", + " )\n", + "\n", + " follower_arms: dict[str, MotorsBusConfig] = field(\n", + " default_factory=lambda: {\n", + " \"main\": DynamixelMotorsBusConfig(\n", + " port=\"/dev/ttyACM_kochfollower\", <-- UPDATE HERE\n", + " motors={\n", + " # name: (index, model)\n", + " \"shoulder_pan\": [1, \"xl430-w250\"],\n", + " \"shoulder_lift\": [2, \"xl430-w250\"],\n", + " \"elbow_flex\": [3, \"xl330-m288\"],\n", + " \"wrist_flex\": [4, \"xl330-m288\"],\n", + " \"wrist_roll\": [5, \"xl330-m288\"],\n", + " \"gripper\": [6, \"xl330-m288\"],\n", + " },\n", + " ),\n", + " }\n", + " )\n", + "```\n", "\n", - "*This configuration file is used to instantiate your robot across all scripts. We'll cover how this works later on.*" + ":::" ] }, { @@ -236,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -245,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -286,18 +339,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 1684 1541 2969 2333 -1150 2645]\n", - "[2068 3183 2864 2337 1015 2080]\n" - ] - } - ], + "outputs": [], "source": [ "leader_pos = leader_arm.read(\"Present_Position\")\n", "follower_pos = follower_arm.read(\"Present_Position\")\n", @@ -323,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -341,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -371,7 +415,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -387,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -443,27 +487,29 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "from lerobot.common.robot_devices.robots.configs import KochRobotConfig\n", "from lerobot.common.robot_devices.robots.manipulator import ManipulatorRobot\n", "\n", - "robot = ManipulatorRobot(\n", - " robot_type=\"koch\",\n", - " leader_arms={\"main\": leader_arm},\n", - " follower_arms={\"main\": follower_arm},\n", - " calibration_dir=\"/opt/lerobot/.cache/calibration/koch\",\n", - ")" + "robot_config = KochRobotConfig(\n", + " leader_arms={\"main\": leader_config},\n", + " follower_arms={\"main\": follower_config},\n", + " cameras={}, # We don't use any camera for now\n", + ")\n", + "robot = ManipulatorRobot(robot_config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "*The `robot_type=\"koch\"` is used to set the associated settings and calibration process. For instance, we activate the torque of the gripper of the leader Koch v1.1 arm and position it at a 40 degree angle to use it as a trigger.*\n", + "*The `KochRobotConfig` is used to set the associated settings and calibration process. For instance, we activate the torque of the gripper of the leader Koch v1.1 arm and position it at a 40 degree angle to use it as a trigger.*\n", "\n", - "For the [Aloha bimanual robot](https://aloha-2.github.io), we would use `robot_type=\"aloha\"` to set different settings such as a secondary ID for shadow joints (shoulder, elbow). Specific to Aloha, LeRobot comes with default calibration files stored in in `.cache/calibration/aloha_default`. Assuming the motors have been properly assembled, no manual calibration step is expected. If you need to run manual calibration, simply update `calibration_dir` to `.cache/calibration/aloha`." + "For the [Aloha bimanual robot](https://aloha-2.github.io), we would use `AlohaRobotConfig` to set different settings such as a secondary ID for shadow joints (shoulder, elbow). Specific to Aloha, LeRobot comes with default calibration files stored in in `.cache/calibration/aloha_default`. Assuming the motors have been properly assembled, no manual calibration step is expected for Aloha.\n", + "" ] }, { @@ -496,6 +542,16 @@ "*You can watch a [video tutorial of the calibration procedure](https://youtu.be/8drnU9uRY24) for more details.*" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%html\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -511,217 +567,101 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "*Run the following code to calibrate and connect your robot:*\n", + "### Run Calibration\n", "\n", + "*Run the following code to calibrate and connect your robot:*\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "robot.connect()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ ":::{dropdown} Output example (click to open)\n", "\n", "```\n", - "Connecting main follower arm\n", - "Connecting main leader arm\n", - "\n", + "Connecting main follower arm.\n", + "Connecting main leader arm.\n", "Missing calibration file '.cache/calibration/koch/main_follower.json'\n", + "\n", "Running calibration of koch main follower...\n", + "\n", "Move arm to zero position\n", - "[...]\n", - "Move arm to rotated position\n", - "[...]\n", + "See: https://raw.githubusercontent.com/huggingface/lerobot/main/media/koch/follower_zero.webp\n", + "Press Enter to continue... \n", + "\n", + "Move arm to rotated target position\n", + "See: https://raw.githubusercontent.com/huggingface/lerobot/main/media/koch/follower_rotated.webp\n", + "Press Enter to continue... \n", + "\n", "Move arm to rest position\n", - "[...]\n", - "Calibration is done! Saving calibration file '.cache/calibration/koch/main_follower.json'\n", + "See: https://raw.githubusercontent.com/huggingface/lerobot/main/media/koch/follower_rest.webp\n", + "Press Enter to continue... \n", "\n", + "Calibration is done! Saving calibration file '.cache/calibration/koch/main_follower.json'\n", "Missing calibration file '.cache/calibration/koch/main_leader.json'\n", + "\n", "Running calibration of koch main leader...\n", + "\n", "Move arm to zero position\n", - "[...]\n", - "Move arm to rotated position\n", - "[...]\n", + "See: https://raw.githubusercontent.com/huggingface/lerobot/main/media/koch/leader_zero.webp\n", + "Press Enter to continue... \n", + "\n", + "Move arm to rotated target position\n", + "See: https://raw.githubusercontent.com/huggingface/lerobot/main/media/koch/leader_rotated.webp\n", + "Press Enter to continue... \n", + "\n", "Move arm to rest position\n", - "[...]\n", + "See: https://raw.githubusercontent.com/huggingface/lerobot/main/media/koch/leader_rest.webp\n", + "Press Enter to continue... \n", + "\n", "Calibration is done! Saving calibration file '.cache/calibration/koch/main_leader.json'\n", + "Activating torque on main follower arm.\n", "```\n", "\n", ":::" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ":::{tip}\n", + "In case you messed up your calibration, you can re-run the calibration by deleting `.cache/calibration/koch/main_leader.json` file.\n", + "\n", + "Run the following **2 code cells**, and go back to `robot.connect()` cell to repeat the calibration process." + ] + }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:127: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n", - "Traceback (most recent call last):\n", - " File \"/opt/lerobot/lerobot/scripts/control_robot.py\", line 872, in \n", - " calibrate(robot, **kwargs)\n", - " File \"/opt/lerobot/lerobot/scripts/control_robot.py\", line 254, in calibrate\n", - " unknown_arms = [arm_id for arm_id in arms if arm_id not in available_arms]\n", - "TypeError: 'NoneType' object is not iterable\n" - ] - } - ], + "outputs": [], "source": [ - "!cd /opt/lerobot && python3 lerobot/scripts/control_robot.py calibrate" + "### ⚠️ Only run this cell when you want to re-run the calibration\n", + "!ls -la .cache/calibration/koch/main_follower.json\n", + "!ls -la .cache/calibration/koch/main_leader.json\n", + "!rm .cache/calibration/koch/main_follower.json\n", + "!rm .cache/calibration/koch/main_leader.json\n", + "!ls -la .cache/calibration/koch/" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting main follower arm.\n", - "Connecting main leader arm.\n", - "Missing calibration file '/opt/lerobot/.cache/calibration/koch/main_follower.json'\n", - "\n", - "Running calibration of koch main follower...\n", - "\n", - "Move arm to zero position\n", - "See: https://raw.githubusercontent.com/huggingface/lerobot/main/media/koch/follower_zero.webp\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Press Enter to continue... \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Move arm to rotated target position\n", - "See: https://raw.githubusercontent.com/huggingface/lerobot/main/media/koch/follower_rotated.webp\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Press Enter to continue... \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Move arm to rest position\n", - "See: https://raw.githubusercontent.com/huggingface/lerobot/main/media/koch/follower_rest.webp\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Press Enter to continue... \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Calibration is done! Saving calibration file '/opt/lerobot/.cache/calibration/koch/main_follower.json'\n", - "Missing calibration file '/opt/lerobot/.cache/calibration/koch/main_leader.json'\n", - "\n", - "Running calibration of koch main leader...\n", - "\n", - "Move arm to zero position\n", - "See: https://raw.githubusercontent.com/huggingface/lerobot/main/media/koch/leader_zero.webp\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Press Enter to continue... \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Move arm to rotated target position\n", - "See: https://raw.githubusercontent.com/huggingface/lerobot/main/media/koch/leader_rotated.webp\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Press Enter to continue... \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Move arm to rest position\n", - "See: https://raw.githubusercontent.com/huggingface/lerobot/main/media/koch/leader_rest.webp\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Press Enter to continue... \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Calibration is done! Saving calibration file '/opt/lerobot/.cache/calibration/koch/main_leader.json'\n", - "Activating torque on main follower arm.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Press Enter to continue... \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Move arm to rest position\n", - "See: https://raw.githubusercontent.com/huggingface/lerobot/main/media/koch/leader_rest.webp\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Press Enter to continue... \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Calibration is done! Saving calibration file '/opt/lerobot/.cache/calibration/koch/main_leader.json'\n", - "Activating torque on main follower arm.\n" - ] - } - ], + "outputs": [], "source": [ - "robot.connect()" + "### ⚠️ Only run this cell when you want to re-run the calibration\n", + "robot.disconnect()" ] }, { @@ -737,32 +677,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Run the following code to get the positions in degrees:\n", - "\n", - ":::{dropdown} Output example (click to open)\n", - "\n", - "```\n", - "array([-0.43945312, 133.94531, 179.82422, -18.984375, -1.9335938, 34.541016], dtype=float32)\n", - "array([-0.58723712, 131.72314, 174.98743, -16.872612, 0.786213, 35.271973], dtype=float32)\n", - "```\n", - "\n", - ":::" + "Run the following code to get the positions in degrees:" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 6.1523438 134.20898 179.29688 -29.794922 -5.361328 55.546875 ]\n", - "[ -1.3183594 128.84766 176.39648 -12.744141 -2.2851562 35.683594 ]\n" - ] - } - ], + "outputs": [], "source": [ "leader_pos = robot.leader_arms[\"main\"].read(\"Present_Position\")\n", "follower_pos = robot.follower_arms[\"main\"].read(\"Present_Position\")\n", @@ -775,6 +697,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ + ":::{dropdown} Example output: (click to open)\n", + "\n", + "```\n", + "array([-0.43945312, 133.94531, 179.82422, -18.984375, -1.9335938, 34.541016], dtype=float32)\n", + "array([-0.58723712, 131.72314, 174.98743, -16.872612, 0.786213, 35.271973], dtype=float32)\n", + "```\n", + "\n", + ":::\n", + "\n", "*These values are in degrees, which makes them easier to interpret and debug. The zero position used during calibration should roughly correspond to 0 degrees for each motor, and the rotated position should roughly correspond to 90 degrees for each motor.*\n" ] }, @@ -796,17 +727,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 6000/6000 [00:31<00:00, 191.27it/s]\n" - ] - } - ], + "outputs": [], "source": [ "import tqdm\n", "seconds = 30\n", @@ -834,18 +757,27 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 6000/6000 [00:31<00:00, 188.65it/s]\n" - ] - } - ], + "outputs": [], "source": [ + "import numpy as np\n", + "import torch\n", + "print(torch.__version__)\n", + "leader_pos_raw = robot.leader_arms[\"main\"].read(\"Present_Position\")\n", + "print(type(leader_pos_raw), type(leader_pos_raw[0]))\n", + "print(np.asarray(leader_pos_raw).shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tqdm\n", + "seconds = 30\n", + "frequency = 200\n", "for _ in tqdm.tqdm(range(seconds*frequency)):\n", " robot.teleop_step()" ] @@ -879,21 +811,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-48.339844 25.488281 29.003906 95.009766 0.17578125\n", - " 85.51758 ]\n", - "{'observation.state': tensor([-48.3398, 25.4883, 29.0039, 95.0098, 0.1758, 85.5176])}\n", - "[-47.72461 41.04492 8.964844 80.771484 5.7128906 -11.25 ]\n", - "{'action': tensor([-47.7246, 41.0449, 8.9648, 80.6836, 5.6250, -11.1621])}\n" - ] - } - ], + "outputs": [], "source": [ "leader_pos = robot.leader_arms[\"main\"].read(\"Present_Position\")\n", "follower_pos = robot.follower_arms[\"main\"].read(\"Present_Position\")\n", @@ -925,22 +845,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "RobotDeviceNotConnectedError", - "evalue": "ManipulatorRobot is not connected. You need to run `robot.connect()` before disconnecting.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRobotDeviceNotConnectedError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[23], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mrobot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdisconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/lerobot/lerobot/common/robot_devices/robots/manipulator.py:686\u001b[0m, in \u001b[0;36mManipulatorRobot.disconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 684\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdisconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 685\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_connected:\n\u001b[0;32m--> 686\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m RobotDeviceNotConnectedError(\n\u001b[1;32m 687\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mManipulatorRobot is not connected. You need to run `robot.connect()` before disconnecting.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 688\u001b[0m )\n\u001b[1;32m 690\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfollower_arms:\n\u001b[1;32m 691\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfollower_arms[name]\u001b[38;5;241m.\u001b[39mdisconnect()\n", - "\u001b[0;31mRobotDeviceNotConnectedError\u001b[0m: ManipulatorRobot is not connected. You need to run `robot.connect()` before disconnecting." - ] - } - ], + "outputs": [], "source": [ "robot.disconnect()" ] @@ -993,112 +900,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux detected. Finding available camera indices through scanning '/dev/video*' ports\n", - "[ WARN:0@0.734] global cap_gstreamer.cpp:1173 isPipelinePlaying OpenCV | GStreamer warning: GStreamer: pipeline have not been created\n", - "Camera found at index 3\n", - "[ WARN:0@0.736] global cap_gstreamer.cpp:1173 isPipelinePlaying OpenCV | GStreamer warning: GStreamer: pipeline have not been created\n", - "Camera found at index 2\n", - "Connecting cameras\n", - "OpenCVCamera(3, fps=25, width=640, height=480, color_mode=rgb)\n", - "OpenCVCamera(2, fps=25, width=640, height=480, color_mode=rgb)\n", - "Saving images to /opt/lerobot/notebooks/outputs/images_from_opencv_cameras\n", - "Frame: 0000\tLatency (ms): 23.99\n", - "Frame: 0001\tLatency (ms): 15.69\n", - "Frame: 0002\tLatency (ms): 14.24\n", - "Frame: 0003\tLatency (ms): 6.53\n", - "Frame: 0004\tLatency (ms): 7.80\n", - "Frame: 0005\tLatency (ms): 7.78\n", - "Frame: 0006\tLatency (ms): 6.54\n", - "Frame: 0007\tLatency (ms): 6.07\n", - "Frame: 0008\tLatency (ms): 6.77\n", - "Frame: 0009\tLatency (ms): 6.63\n", - "Frame: 0010\tLatency (ms): 6.39\n", - "Frame: 0011\tLatency (ms): 6.21\n", - "Frame: 0012\tLatency (ms): 5.98\n", - "Frame: 0013\tLatency (ms): 6.21\n", - "Frame: 0014\tLatency (ms): 6.04\n", - "Frame: 0015\tLatency (ms): 5.90\n", - "Frame: 0016\tLatency (ms): 6.31\n", - "Frame: 0017\tLatency (ms): 6.12\n", - "Frame: 0018\tLatency (ms): 5.78\n", - "Frame: 0019\tLatency (ms): 6.32\n", - "Frame: 0020\tLatency (ms): 6.57\n", - "Frame: 0021\tLatency (ms): 6.88\n", - "Frame: 0022\tLatency (ms): 5.73\n", - "Frame: 0023\tLatency (ms): 6.80\n", - "Frame: 0024\tLatency (ms): 6.21\n", - "Frame: 0025\tLatency (ms): 6.49\n", - "Frame: 0026\tLatency (ms): 6.31\n", - "Frame: 0027\tLatency (ms): 6.40\n", - "Frame: 0028\tLatency (ms): 6.65\n", - "Frame: 0029\tLatency (ms): 6.40\n", - "Frame: 0030\tLatency (ms): 6.63\n", - "Frame: 0031\tLatency (ms): 7.02\n", - "Frame: 0032\tLatency (ms): 6.33\n", - "Frame: 0033\tLatency (ms): 5.98\n", - "Frame: 0034\tLatency (ms): 27.82\n", - "Frame: 0035\tLatency (ms): 28.70\n", - "Frame: 0036\tLatency (ms): 40.76\n", - "Frame: 0037\tLatency (ms): 28.45\n", - "Frame: 0038\tLatency (ms): 31.16\n", - "Frame: 0039\tLatency (ms): 27.04\n", - "Frame: 0040\tLatency (ms): 34.41\n", - "Frame: 0041\tLatency (ms): 35.54\n", - "Frame: 0042\tLatency (ms): 36.80\n", - "Frame: 0043\tLatency (ms): 45.02\n", - "Frame: 0044\tLatency (ms): 36.47\n", - "Frame: 0045\tLatency (ms): 25.97\n", - "Frame: 0046\tLatency (ms): 25.30\n", - "Frame: 0047\tLatency (ms): 26.79\n", - "Frame: 0048\tLatency (ms): 41.82\n", - "Frame: 0049\tLatency (ms): 42.18\n", - "Frame: 0050\tLatency (ms): 25.77\n", - "Frame: 0051\tLatency (ms): 33.27\n", - "Frame: 0052\tLatency (ms): 55.49\n", - "Frame: 0053\tLatency (ms): 6.33\n", - "Frame: 0054\tLatency (ms): 31.73\n", - "Frame: 0055\tLatency (ms): 39.57\n", - "Frame: 0056\tLatency (ms): 21.89\n", - "Frame: 0057\tLatency (ms): 45.43\n", - "Frame: 0058\tLatency (ms): 27.73\n", - "Frame: 0059\tLatency (ms): 36.65\n", - "Frame: 0060\tLatency (ms): 33.34\n", - "Frame: 0061\tLatency (ms): 35.53\n", - "Frame: 0062\tLatency (ms): 32.54\n", - "Frame: 0063\tLatency (ms): 23.43\n", - "Frame: 0064\tLatency (ms): 33.65\n", - "Frame: 0065\tLatency (ms): 39.05\n", - "Frame: 0066\tLatency (ms): 25.81\n", - "Frame: 0067\tLatency (ms): 46.11\n", - "Frame: 0068\tLatency (ms): 27.62\n", - "Frame: 0069\tLatency (ms): 36.00\n", - "Frame: 0070\tLatency (ms): 26.04\n", - "Frame: 0071\tLatency (ms): 31.67\n", - "Frame: 0072\tLatency (ms): 32.54\n", - "Frame: 0073\tLatency (ms): 34.36\n", - "Frame: 0074\tLatency (ms): 35.48\n", - "Frame: 0075\tLatency (ms): 30.00\n", - "Frame: 0076\tLatency (ms): 34.25\n", - "Frame: 0077\tLatency (ms): 32.44\n", - "Frame: 0078\tLatency (ms): 42.16\n", - "Frame: 0079\tLatency (ms): 32.90\n", - "Frame: 0080\tLatency (ms): 23.75\n", - "Frame: 0081\tLatency (ms): 39.75\n", - "Frame: 0082\tLatency (ms): 35.50\n", - "Frame: 0083\tLatency (ms): 31.38\n", - "Frame: 0084\tLatency (ms): 25.73\n", - "Frame: 0085\tLatency (ms): 42.82\n", - "Images have been saved to /opt/lerobot/notebooks/outputs/images_from_opencv_cameras\n" - ] - } - ], + "outputs": [], "source": [ "!python3 /opt/lerobot/lerobot/common/robot_devices/cameras/opencv.py \\\n", " --images-dir /opt/lerobot/notebooks/outputs/images_from_opencv_cameras" @@ -1167,20 +971,73 @@ "name": "stdout", "output_type": "stream", "text": [ - "(480, 640, 3)\n", - "uint8\n" + "\n", + "(480, 640, 3)\n" + ] + }, + { + "ename": "TypeError", + "evalue": "expected 0 arguments, got 1", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 17\u001b[0m\n\u001b[1;32m 15\u001b[0m builtins\u001b[38;5;241m.\u001b[39mprint(\u001b[38;5;28mtype\u001b[39m(color_image))\n\u001b[1;32m 16\u001b[0m builtins\u001b[38;5;241m.\u001b[39mprint(color_image\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m---> 17\u001b[0m \u001b[43mbuiltins\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolor_image\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mTypeError\u001b[0m: expected 0 arguments, got 1" ] } ], "source": [ + "from lerobot.common.robot_devices.cameras.configs import OpenCVCameraConfig\n", "from lerobot.common.robot_devices.cameras.opencv import OpenCVCamera\n", "\n", - "camera = OpenCVCamera(camera_index=2)\n", + "# In case print got shadowed earlier\n", + "import builtins\n", + "print = builtins.print\n", + "\n", + "config = OpenCVCameraConfig(camera_index=0)\n", + "camera = OpenCVCamera(config)\n", "camera.connect()\n", "color_image = camera.read()\n", "\n", - "print(color_image.shape)\n", - "print(color_image.dtype)" + "import builtins\n", + "\n", + "builtins.print(type(color_image))\n", + "builtins.print(color_image.shape)\n", + "builtins.print(color_image.dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "type(color_image): \n", + "dir(color_image): ['T', '__abs__', '__add__', '__and__', '__array__', '__array_finalize__', '__array_function__', '__array_interface__', '__array_prepare__', '__array_priority__', '__array_struct__', '__array_ufunc__', '__array_wrap__', '__bool__', '__class__', '__class_getitem__', '__complex__', '__contains__', '__copy__', '__deepcopy__', '__delattr__', '__delitem__', '__dir__', '__divmod__', '__dlpack__', '__dlpack_device__', '__doc__', '__eq__', '__float__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__iand__', '__ifloordiv__', '__ilshift__', '__imatmul__', '__imod__', '__imul__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__ior__', '__ipow__', '__irshift__', '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__', '__len__', '__lshift__', '__lt__', '__matmul__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmatmul__', '__rmod__', '__rmul__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__setitem__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__xor__', 'all', 'any', 'argmax', 'argmin', 'argpartition', 'argsort', 'astype', 'base', 'byteswap', 'choose', 'clip', 'compress', 'conj', 'conjugate', 'copy', 'ctypes', 'cumprod', 'cumsum', 'data', 'diagonal', 'dot', 'dtype', 'dump', 'dumps', 'fill', 'flags', 'flat', 'flatten', 'getfield', 'imag', 'item', 'itemset', 'itemsize', 'max', 'mean', 'min', 'nbytes', 'ndim', 'newbyteorder', 'nonzero', 'partition', 'prod', 'ptp', 'put', 'ravel', 'real', 'repeat', 'reshape', 'resize', 'round', 'searchsorted', 'setfield', 'setflags', 'shape', 'size', 'sort', 'squeeze', 'std', 'strides', 'sum', 'swapaxes', 'take', 'tobytes', 'tofile', 'tolist', 'tostring', 'trace', 'transpose', 'var', 'view']\n", + "color_image.dtype = " + ] + }, + { + "ename": "TypeError", + "evalue": "expected 0 arguments, got 1", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype(color_image):\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mtype\u001b[39m(color_image))\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdir(color_image):\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mdir\u001b[39m(color_image))\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolor_image.dtype =\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcolor_image\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdtype\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallable(color_image.dtype):\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mcallable\u001b[39m(\u001b[38;5;28mgetattr\u001b[39m(color_image, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m'\u001b[39m)))\n", + "\u001b[0;31mTypeError\u001b[0m: expected 0 arguments, got 1" + ] + } + ], + "source": [ + "print(\"type(color_image):\", type(color_image))\n", + "print(\"dir(color_image):\", dir(color_image))\n", + "print(\"color_image.dtype =\", getattr(color_image, 'dtype'))\n", + "print(\"callable(color_image.dtype):\", callable(getattr(color_image, 'dtype')))" ] }, { @@ -1192,12 +1049,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1211,7 +1068,8 @@ "from matplotlib import pyplot as plt\n", "from lerobot.common.robot_devices.cameras.opencv import OpenCVCamera\n", "\n", - "camera = OpenCVCamera(camera_index=2)\n", + "config = OpenCVCameraConfig(camera_index=2)\n", + "camera = OpenCVCamera(config)\n", "camera.connect()\n", "color_image = camera.read()\n", "\n", @@ -1228,7 +1086,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -1248,7 +1106,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1262,15 +1120,19 @@ } ], "source": [ + "from lerobot.common.robot_devices.robots.configs import KochRobotConfig\n", + "from lerobot.common.robot_devices.robots.manipulator import ManipulatorRobot\n", + "\n", "robot = ManipulatorRobot(\n", - " robot_type=\"koch\",\n", - " leader_arms={\"main\": leader_arm},\n", - " follower_arms={\"main\": follower_arm},\n", - " calibration_dir=\"/opt/lerobot/.cache/calibration/koch\",\n", - " cameras={\n", - " \"laptop\": OpenCVCamera(3, fps=25, width=640, height=480),\n", - " \"phone\": OpenCVCamera(2, fps=25, width=640, height=480),\n", - " },\n", + " KochRobotConfig(\n", + " leader_arms={\"main\": leader_config},\n", + " follower_arms={\"main\": follower_config},\n", + " calibration_dir=\".cache/calibration/koch\",\n", + " cameras={\n", + " \"laptop\": OpenCVCameraConfig(2, fps=30, width=640, height=480),\n", + " \"phone\": OpenCVCameraConfig(0, fps=30, width=640, height=480),\n", + " },\n", + " )\n", ")\n", "robot.connect()" ] @@ -1291,7 +1153,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1336,18420 +1198,86 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Also, update the following lines of the yaml file for Koch robot [`lerobot/configs/robot/koch.yaml`](../lerobot/configs/robot/koch.yaml) with the names and configurations of your cameras:\n", - "\n", - "```yaml\n", - "[...]\n", - "cameras:\n", - " laptop:\n", - " _target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera\n", - " camera_index: 0\n", - " fps: 30\n", - " width: 640\n", - " height: 480\n", - " phone:\n", - " _target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera\n", - " camera_index: 1\n", - " fps: 30\n", - " width: 640\n", - " height: 480\n", + "Lastly, open [`../lerobot/common/robot_devices/robots/configs.py`](../lerobot/common/robot_devices/robots/configs.py) and updates the cameras config.\n", + "\n", + "It should look something like:\n", + "\n", + ":::{dropdown} configs.py example (click to open)\n", + "\n", + "```python\n", + "@RobotConfig.register_subclass(\"koch\")\n", + "@dataclass\n", + "class KochRobotConfig(ManipulatorRobotConfig):\n", + " calibration_dir: str = \".cache/calibration/koch\"\n", + " # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.\n", + " # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as\n", + " # the number of motors in your follower arms.\n", + " max_relative_target: int | None = None\n", + "\n", + " leader_arms: ...\n", + " follower_arms: ...\n", + " \n", + " cameras: dict[str, CameraConfig] = field(\n", + " default_factory=lambda: {\n", + " \"laptop\": OpenCVCameraConfig(\n", + " camera_index=2,\n", + " fps=30,\n", + " width=640,\n", + " height=480,\n", + " ),\n", + " \"phone\": OpenCVCameraConfig(\n", + " camera_index=0,\n", + " fps=30,\n", + " width=640,\n", + " height=480,\n", + " ),\n", + " }\n", + " )\n", "```\n", "\n", - "*This file is used to instantiate your robot in all our scripts. We will explain how this works in the next section.*" + ":::" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## d. Use `koch.yaml` and our `teleoperate` function\n", + "## d. Use `control_robot.py` and our `teleoperate` function\n", "\n", - "*Instead of manually running the python code in a terminal window, you can use [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) to instantiate your robot by providing the path to the robot yaml file (e.g. [`lerobot/configs/robot/koch.yaml`](../lerobot/configs/robot/koch.yaml)) and control your robot with various modes as explained next.*\n", + "*Instead of manually running the python code in a terminal window, you can use [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) to instantiate your robot by providing the **robot configurations via command line** and control your robot with various modes as explained next.*\n", "\n", "*Try running this code to teleoperate your robot (if you dont have a camera, keep reading):*" ] }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:127: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n", - "Connecting main follower arm.\n", - "Connecting main leader arm.\n", - "Activating torque on main follower arm.\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 8.99 (111.3hz) dtRlead: 6.73 (148.6hz) dtWfoll: 2.04 (490.8hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 8.19 (122.1hz) dtRlead: 6.47 (154.5hz) dtWfoll: 1.60 (624.6hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 7.38 (135.5hz) dtRlead: 6.05 (165.4hz) dtWfoll: 1.24 (803.8hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 7.52 (133.1hz) dtRlead: 6.13 (163.2hz) dtWfoll: 1.29 (777.6hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 7.24 (138.1hz) dtRlead: 5.73 (174.4hz) dtWfoll: 1.43 (701.3hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 7.12 (140.5hz) dtRlead: 5.80 (172.5hz) dtWfoll: 1.23 (811.4hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 7.35 (136.1hz) dtRlead: 5.80 (172.3hz) dtWfoll: 1.45 (690.7hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 7.04 (142.1hz) dtRlead: 5.65 (177.1hz) dtWfoll: 1.31 (763.3hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.99 (143.1hz) dtRlead: 5.76 (173.7hz) dtWfoll: 1.16 (864.3hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.87 (145.6hz) dtRlead: 5.63 (177.8hz) dtWfoll: 1.18 (847.9hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.90 (144.9hz) dtRlead: 5.69 (175.8hz) dtWfoll: 1.14 (879.8hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.86 (145.7hz) dtRlead: 5.62 (177.8hz) dtWfoll: 1.17 (853.1hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.96 (143.7hz) dtRlead: 5.65 (177.0hz) dtWfoll: 1.24 (808.9hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.66 (150.2hz) dtRlead: 5.53 (181.0hz) dtWfoll: 1.07 (932.8hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.90 (145.0hz) dtRlead: 5.59 (178.9hz) dtWfoll: 1.22 (820.3hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 7.53 (132.8hz) dtRlead: 6.14 (162.8hz) dtWfoll: 1.30 (771.9hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.87 (145.6hz) dtRlead: 5.68 (176.2hz) dtWfoll: 1.12 (892.0hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.63 (150.9hz) dtRlead: 5.44 (183.8hz) dtWfoll: 1.13 (883.8hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.86 (145.8hz) dtRlead: 5.62 (178.1hz) dtWfoll: 1.18 (848.6hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 8.36 (119.6hz) dtRlead: 7.15 (139.8hz) dtWfoll: 1.13 (884.0hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 7.17 (139.4hz) dtRlead: 5.69 (175.8hz) dtWfoll: 1.40 (715.2hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.94 (144.0hz) dtRlead: 5.68 (175.9hz) dtWfoll: 1.18 (846.8hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 7.21 (138.8hz) dtRlead: 5.71 (175.1hz) dtWfoll: 1.42 (706.0hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.91 (144.8hz) dtRlead: 5.62 (178.1hz) dtWfoll: 1.22 (822.9hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.90 (145.0hz) dtRlead: 5.63 (177.8hz) dtWfoll: 1.20 (834.4hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.90 (144.8hz) dtRlead: 5.68 (176.1hz) dtWfoll: 1.15 (866.0hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.82 (146.7hz) dtRlead: 5.60 (178.7hz) dtWfoll: 1.16 (863.9hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.61 (151.3hz) dtRlead: 5.52 (181.2hz) dtWfoll: 1.03 (966.5hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 7.52 (133.0hz) dtRlead: 6.29 (158.9hz) dtWfoll: 1.15 (865.9hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.89 (145.1hz) dtRlead: 5.66 (176.7hz) dtWfoll: 1.16 (861.4hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.70 (149.4hz) dtRlead: 5.52 (181.2hz) dtWfoll: 1.11 (902.9hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.77 (147.7hz) dtRlead: 5.59 (178.9hz) dtWfoll: 1.12 (896.5hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 7.02 (142.5hz) dtRlead: 5.47 (182.9hz) dtWfoll: 1.48 (674.6hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.99 (143.1hz) dtRlead: 5.75 (173.8hz) dtWfoll: 1.16 (861.2hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 7.17 (139.5hz) dtRlead: 5.63 (177.5hz) dtWfoll: 1.45 (687.4hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.99 (143.2hz) dtRlead: 5.62 (178.0hz) dtWfoll: 1.30 (771.1hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.73 (148.6hz) dtRlead: 5.57 (179.6hz) dtWfoll: 1.09 (915.7hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.93 (144.2hz) dtRlead: 5.70 (175.5hz) dtWfoll: 1.16 (860.5hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.69 (149.4hz) dtRlead: 5.51 (181.4hz) dtWfoll: 1.12 (893.4hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.82 (146.7hz) dtRlead: 5.64 (177.4hz) dtWfoll: 1.11 (904.0hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.91 (144.8hz) dtRlead: 5.62 (177.8hz) dtWfoll: 1.22 (821.4hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.56 (152.4hz) dtRlead: 5.46 (183.0hz) dtWfoll: 1.04 (962.2hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 7.03 (142.2hz) dtRlead: 5.80 (172.4hz) dtWfoll: 1.16 (860.7hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.68 (149.7hz) dtRlead: 5.47 (182.8hz) dtWfoll: 1.14 (875.3hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.78 (147.5hz) dtRlead: 5.62 (177.9hz) dtWfoll: 1.09 (915.4hz)\n", - "INFO 2024-10-02 00:59:54 ol_robot.py:218 dt: 6.92 (144.5hz) dtRlead: 5.72 (174.9hz) dtWfoll: 1.13 (886.7hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.60 (151.4hz) dtRlead: 5.44 (183.8hz) dtWfoll: 1.08 (924.9hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.75 (148.1hz) dtRlead: 5.60 (178.5hz) dtWfoll: 1.08 (924.3hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.88 (145.4hz) dtRlead: 5.64 (177.5hz) dtWfoll: 1.16 (859.4hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.95 (143.9hz) dtRlead: 5.68 (176.1hz) dtWfoll: 1.19 (838.9hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.71 (149.0hz) dtRlead: 5.52 (181.3hz) dtWfoll: 1.12 (888.9hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.81 (146.9hz) dtRlead: 5.54 (180.7hz) dtWfoll: 1.17 (851.1hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.77 (147.6hz) dtRlead: 5.61 (178.3hz) dtWfoll: 1.09 (915.1hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.86 (145.8hz) dtRlead: 5.66 (176.8hz) dtWfoll: 1.13 (883.7hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.85 (146.0hz) dtRlead: 5.68 (176.0hz) dtWfoll: 1.09 (914.7hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.86 (145.7hz) dtRlead: 5.65 (177.1hz) dtWfoll: 1.15 (871.5hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 7.17 (139.5hz) dtRlead: 5.88 (169.9hz) dtWfoll: 1.21 (829.7hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.99 (143.0hz) dtRlead: 5.60 (178.4hz) dtWfoll: 1.31 (764.6hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.87 (145.6hz) dtRlead: 5.67 (176.3hz) dtWfoll: 1.12 (894.2hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.87 (145.6hz) dtRlead: 5.62 (177.9hz) dtWfoll: 1.17 (856.4hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.87 (145.5hz) dtRlead: 5.58 (179.1hz) dtWfoll: 1.21 (825.9hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 7.14 (140.1hz) dtRlead: 5.78 (173.1hz) dtWfoll: 1.25 (800.3hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.89 (145.2hz) dtRlead: 5.68 (176.1hz) dtWfoll: 1.13 (884.3hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.82 (146.7hz) dtRlead: 5.63 (177.6hz) dtWfoll: 1.12 (893.7hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 7.13 (140.2hz) dtRlead: 5.64 (177.3hz) dtWfoll: 1.42 (706.2hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.82 (146.6hz) dtRlead: 5.59 (178.9hz) dtWfoll: 1.15 (867.7hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.70 (149.3hz) dtRlead: 5.53 (180.7hz) dtWfoll: 1.09 (914.7hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.86 (145.7hz) dtRlead: 5.66 (176.6hz) dtWfoll: 1.13 (887.3hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.75 (148.1hz) dtRlead: 5.55 (180.1hz) dtWfoll: 1.13 (886.7hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.76 (147.9hz) dtRlead: 5.55 (180.1hz) dtWfoll: 1.14 (877.6hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.83 (146.5hz) dtRlead: 5.63 (177.7hz) dtWfoll: 1.13 (888.1hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.68 (149.7hz) dtRlead: 5.48 (182.4hz) dtWfoll: 1.12 (889.5hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.87 (145.6hz) dtRlead: 5.65 (177.1hz) dtWfoll: 1.14 (874.5hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.72 (148.8hz) dtRlead: 5.52 (181.3hz) dtWfoll: 1.14 (879.7hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.59 (151.7hz) dtRlead: 5.48 (182.5hz) dtWfoll: 1.05 (948.6hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.87 (145.6hz) dtRlead: 5.65 (177.0hz) dtWfoll: 1.13 (883.9hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.77 (147.7hz) dtRlead: 5.54 (180.4hz) dtWfoll: 1.15 (866.9hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.74 (148.4hz) dtRlead: 5.51 (181.5hz) dtWfoll: 1.15 (867.2hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.64 (150.6hz) dtRlead: 5.46 (183.0hz) dtWfoll: 1.11 (899.6hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.63 (150.8hz) dtRlead: 5.49 (182.0hz) dtWfoll: 1.05 (948.4hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.90 (145.0hz) dtRlead: 5.66 (176.8hz) dtWfoll: 1.16 (862.2hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.87 (145.5hz) dtRlead: 5.61 (178.2hz) dtWfoll: 1.19 (843.8hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.74 (148.4hz) dtRlead: 5.46 (183.2hz) dtWfoll: 1.21 (826.7hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.75 (148.2hz) dtRlead: 5.55 (180.1hz) dtWfoll: 1.12 (890.4hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.97 (143.5hz) dtRlead: 5.64 (177.4hz) dtWfoll: 1.24 (803.5hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 7.08 (141.3hz) dtRlead: 5.80 (172.4hz) dtWfoll: 1.20 (835.7hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.90 (144.9hz) dtRlead: 5.65 (177.0hz) dtWfoll: 1.18 (849.1hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.72 (148.9hz) dtRlead: 5.54 (180.6hz) dtWfoll: 1.11 (902.9hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.78 (147.6hz) dtRlead: 5.61 (178.4hz) dtWfoll: 1.10 (909.3hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.86 (145.7hz) dtRlead: 5.67 (176.3hz) dtWfoll: 1.13 (886.3hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.91 (144.6hz) dtRlead: 5.65 (176.9hz) dtWfoll: 1.19 (841.7hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.70 (149.2hz) dtRlead: 5.54 (180.3hz) dtWfoll: 1.09 (917.4hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.70 (149.3hz) dtRlead: 5.53 (180.8hz) dtWfoll: 1.10 (907.8hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.85 (145.9hz) dtRlead: 5.67 (176.5hz) dtWfoll: 1.12 (894.0hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.96 (143.7hz) dtRlead: 5.63 (177.6hz) dtWfoll: 1.25 (798.0hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.89 (145.2hz) dtRlead: 5.60 (178.5hz) dtWfoll: 1.20 (830.4hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.73 (148.6hz) dtRlead: 5.58 (179.3hz) dtWfoll: 1.08 (923.8hz)\n", - "INFO 2024-10-02 00:59:55 ol_robot.py:218 dt: 6.68 (149.6hz) dtRlead: 5.52 (181.2hz) dtWfoll: 1.10 (912.2hz)\n", - 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"INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.69 (149.4hz) dtRlead: 5.54 (180.5hz) dtWfoll: 1.10 (912.2hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 7.03 (142.3hz) dtRlead: 5.58 (179.2hz) dtWfoll: 1.38 (725.5hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.55 (152.6hz) dtRlead: 5.44 (183.8hz) dtWfoll: 1.05 (949.4hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.80 (147.1hz) dtRlead: 5.60 (178.5hz) dtWfoll: 1.11 (899.8hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.62 (151.0hz) dtRlead: 5.50 (181.9hz) dtWfoll: 1.07 (938.8hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.78 (147.4hz) dtRlead: 5.64 (177.2hz) dtWfoll: 1.08 (925.7hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.74 (148.4hz) dtRlead: 5.54 (180.5hz) dtWfoll: 1.13 (885.1hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.61 (151.4hz) dtRlead: 5.50 (181.7hz) dtWfoll: 1.04 (957.7hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.73 (148.6hz) dtRlead: 5.57 (179.4hz) dtWfoll: 1.09 (921.3hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.57 (152.1hz) dtRlead: 5.44 (183.8hz) dtWfoll: 1.08 (928.0hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.51 (153.7hz) dtRlead: 5.42 (184.5hz) dtWfoll: 1.03 (967.0hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 7.04 (142.0hz) dtRlead: 5.77 (173.2hz) dtWfoll: 1.18 (845.4hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.61 (151.4hz) dtRlead: 5.44 (183.9hz) dtWfoll: 1.11 (900.2hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.81 (146.8hz) dtRlead: 5.55 (180.3hz) dtWfoll: 1.20 (836.2hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.55 (152.7hz) dtRlead: 5.42 (184.6hz) dtWfoll: 1.07 (930.5hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.66 (150.2hz) dtRlead: 5.59 (178.8hz) dtWfoll: 1.01 (986.7hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.58 (151.9hz) dtRlead: 5.42 (184.6hz) dtWfoll: 1.11 (903.1hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.47 (154.6hz) dtRlead: 5.36 (186.6hz) dtWfoll: 1.06 (946.4hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.69 (149.5hz) dtRlead: 5.50 (181.9hz) dtWfoll: 1.13 (887.0hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.63 (150.7hz) dtRlead: 5.48 (182.4hz) dtWfoll: 1.10 (911.8hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.60 (151.5hz) dtRlead: 5.35 (186.8hz) dtWfoll: 1.20 (836.7hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.53 (153.1hz) dtRlead: 5.44 (183.8hz) dtWfoll: 1.04 (963.3hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.60 (151.4hz) dtRlead: 5.42 (184.6hz) dtWfoll: 1.14 (879.1hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.57 (152.3hz) dtRlead: 5.46 (183.0hz) dtWfoll: 1.05 (951.8hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 7.06 (141.6hz) dtRlead: 5.41 (185.0hz) dtWfoll: 1.60 (624.8hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.52 (153.3hz) dtRlead: 5.43 (184.1hz) dtWfoll: 1.04 (964.0hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.56 (152.5hz) dtRlead: 5.41 (184.9hz) dtWfoll: 1.10 (909.4hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.81 (146.8hz) dtRlead: 5.62 (178.0hz) dtWfoll: 1.13 (884.0hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.56 (152.5hz) dtRlead: 5.45 (183.3hz) dtWfoll: 1.05 (954.0hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.50 (153.9hz) dtRlead: 5.41 (184.9hz) dtWfoll: 1.03 (967.8hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.80 (147.0hz) dtRlead: 5.42 (184.5hz) dtWfoll: 1.32 (758.7hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.56 (152.4hz) dtRlead: 5.46 (183.1hz) dtWfoll: 1.04 (957.6hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.57 (152.2hz) dtRlead: 5.45 (183.3hz) dtWfoll: 1.06 (947.6hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.54 (152.9hz) dtRlead: 5.40 (185.2hz) dtWfoll: 1.08 (923.0hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.53 (153.1hz) dtRlead: 5.42 (184.5hz) dtWfoll: 1.05 (948.8hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.55 (152.6hz) dtRlead: 5.46 (183.3hz) dtWfoll: 1.05 (956.6hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.52 (153.3hz) dtRlead: 5.39 (185.6hz) dtWfoll: 1.08 (922.4hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.48 (154.2hz) dtRlead: 5.42 (184.7hz) dtWfoll: 1.02 (981.3hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.64 (150.6hz) dtRlead: 5.50 (181.8hz) dtWfoll: 1.08 (926.2hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.51 (153.7hz) dtRlead: 5.43 (184.1hz) dtWfoll: 1.02 (978.4hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.44 (155.2hz) dtRlead: 5.38 (186.0hz) dtWfoll: 1.02 (984.1hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.80 (147.0hz) dtRlead: 5.51 (181.4hz) dtWfoll: 1.23 (810.1hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.72 (148.8hz) dtRlead: 5.55 (180.2hz) dtWfoll: 1.11 (902.8hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.74 (148.5hz) dtRlead: 5.46 (183.2hz) dtWfoll: 1.22 (821.0hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.46 (154.7hz) dtRlead: 5.37 (186.3hz) dtWfoll: 1.05 (956.0hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.57 (152.2hz) dtRlead: 5.42 (184.4hz) dtWfoll: 1.10 (912.4hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.50 (153.9hz) dtRlead: 5.36 (186.7hz) dtWfoll: 1.09 (917.0hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.56 (152.4hz) dtRlead: 5.40 (185.3hz) dtWfoll: 1.11 (900.1hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.55 (152.7hz) dtRlead: 5.43 (184.2hz) dtWfoll: 1.07 (937.4hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.80 (147.0hz) dtRlead: 5.69 (175.8hz) dtWfoll: 1.06 (946.1hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 7.00 (142.9hz) dtRlead: 5.65 (176.9hz) dtWfoll: 1.27 (785.9hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.58 (151.9hz) dtRlead: 5.46 (183.3hz) dtWfoll: 1.07 (930.6hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.86 (145.8hz) dtRlead: 5.67 (176.5hz) dtWfoll: 1.13 (888.2hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.60 (151.5hz) dtRlead: 5.50 (182.0hz) dtWfoll: 1.05 (949.5hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.49 (154.1hz) dtRlead: 5.43 (184.0hz) dtWfoll: 1.01 (994.1hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.98 (143.3hz) dtRlead: 5.76 (173.6hz) dtWfoll: 1.15 (871.5hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.60 (151.4hz) dtRlead: 5.42 (184.4hz) dtWfoll: 1.12 (892.0hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.88 (145.4hz) dtRlead: 5.64 (177.3hz) dtWfoll: 1.17 (855.6hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.52 (153.3hz) dtRlead: 5.43 (184.2hz) dtWfoll: 1.04 (961.2hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.43 (155.5hz) dtRlead: 5.36 (186.7hz) dtWfoll: 1.01 (990.5hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 7.15 (139.8hz) dtRlead: 5.91 (169.2hz) dtWfoll: 1.17 (856.5hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.63 (150.7hz) dtRlead: 5.45 (183.4hz) dtWfoll: 1.13 (887.9hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.60 (151.6hz) dtRlead: 5.47 (182.7hz) dtWfoll: 1.06 (940.5hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.63 (150.8hz) dtRlead: 5.42 (184.6hz) dtWfoll: 1.16 (858.8hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 7.07 (141.4hz) dtRlead: 5.67 (176.2hz) dtWfoll: 1.32 (759.8hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.74 (148.3hz) dtRlead: 5.53 (180.7hz) dtWfoll: 1.14 (878.0hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.51 (153.6hz) dtRlead: 5.41 (184.7hz) dtWfoll: 1.05 (955.9hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.64 (150.6hz) dtRlead: 5.48 (182.6hz) dtWfoll: 1.11 (902.4hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.46 (154.7hz) dtRlead: 5.41 (184.9hz) dtWfoll: 1.01 (993.5hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.41 (156.0hz) dtRlead: 5.33 (187.5hz) dtWfoll: 1.03 (973.7hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.94 (144.2hz) dtRlead: 5.57 (179.6hz) dtWfoll: 1.31 (764.3hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.80 (147.1hz) dtRlead: 5.65 (176.9hz) dtWfoll: 1.09 (920.8hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 7.71 (129.8hz) dtRlead: 5.69 (175.7hz) dtWfoll: 1.93 (517.8hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 8.94 (111.9hz) dtRlead: 5.58 (179.3hz) dtWfoll: 3.28 (305.2hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 7.63 (131.0hz) dtRlead: 6.35 (157.4hz) dtWfoll: 1.20 (833.1hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.54 (152.8hz) dtRlead: 5.39 (185.6hz) dtWfoll: 1.10 (908.6hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.61 (151.3hz) dtRlead: 5.40 (185.3hz) dtWfoll: 1.15 (867.4hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.74 (148.3hz) dtRlead: 5.54 (180.6hz) dtWfoll: 1.15 (867.5hz)\n", - "INFO 2024-10-02 01:02:02 ol_robot.py:218 dt: 6.92 (144.6hz) dtRlead: 5.39 (185.7hz) dtWfoll: 1.48 (676.4hz)\n", - "^C\n", "Traceback (most recent call last):\n", - " File \"/opt/lerobot/lerobot/scripts/control_robot.py\", line 875, in \n", - " teleoperate(robot, **kwargs)\n", - " File \"/opt/lerobot/lerobot/scripts/control_robot.py\", line 296, in teleoperate\n", - " robot.teleop_step()\n", - " File \"/opt/lerobot/lerobot/common/robot_devices/robots/manipulator.py\", line 558, in teleop_step\n", - " self.follower_arms[name].write(\"Goal_Position\", goal_pos)\n", - " File \"/opt/lerobot/lerobot/common/robot_devices/motors/dynamixel.py\", line 946, in write\n", - " comm = self.group_writers[group_key].txPacket()\n", - " File \"/usr/local/lib/python3.10/dist-packages/dynamixel_sdk/group_sync_write.py\", line 93, in txPacket\n", - " return self.ph.syncWriteTxOnly(self.port, self.start_address, self.data_length, self.param,\n", - " File \"/usr/local/lib/python3.10/dist-packages/dynamixel_sdk/protocol2_packet_handler.py\", line 750, in syncWriteTxOnly\n", - " _, result, _ = self.txRxPacket(port, txpacket)\n", - " File \"/usr/local/lib/python3.10/dist-packages/dynamixel_sdk/protocol2_packet_handler.py\", line 323, in txRxPacket\n", - " result = self.txPacket(port, txpacket)\n", - " File \"/usr/local/lib/python3.10/dist-packages/dynamixel_sdk/protocol2_packet_handler.py\", line 221, in txPacket\n", - " total_packet_length = DXL_MAKEWORD(txpacket[PKT_LENGTH_L], txpacket[PKT_LENGTH_H]) + 7\n", - "KeyboardInterrupt\n" + " File \"/opt/lerobot/lerobot/scripts/control_robot.py\", line 123, in \n", + " from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset\n", + " File \"/opt/lerobot/lerobot/common/datasets/lerobot_dataset.py\", line 34, in \n", + " from lerobot.common.datasets.compute_stats import aggregate_stats, compute_episode_stats\n", + "ImportError: cannot import name 'compute_episode_stats' from 'lerobot.common.datasets.compute_stats' (/opt/lerobot/lerobot/common/datasets/compute_stats.py)\n" ] } ], "source": [ - "!python3 /opt/lerobot/lerobot/scripts/control_robot.py teleoperate \\\n", - " --robot-path /opt/lerobot/lerobot/configs/robot/koch.yaml" + "!python3 /opt/lerobot/lerobot/scripts/control_robot.py \\\n", + " --robot.type=koch \\\n", + " --control.type=teleoperate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You should see a lot of lines appearing like this one:\n", + "*You should see a lot of lines appearing like this one:*\n", "\n", ":::{dropdown} Expected output\n", ":open:\n", @@ -19765,7 +1293,17 @@ "- `ol_robot.py:209` which is the end of the file name and the line number where the print function is called (`lerobot/scripts/control_robot.py` line `209`).\n", "- `dt: 5.12 (195.1hz)` which is the \"delta time\" or the number of milliseconds spent between the previous call to `robot.teleop_step()` and the current one, associated with the frequency (5.12 ms equals 195.1 Hz) ; note that you can control the maximum frequency by adding fps as argument such as `--fps 30`.\n", "- `dtRlead: 4.93 (203.0hz)` which is the number of milliseconds it took to read the position of the leader arm using `leader_arm.read(\"Present_Position\")`.\n", - "- `dtWfoll: 0.22 (4446.9hz)` which is the number of milliseconds it took to set a new goal position for the follower arm using `follower_arm.write(\"Goal_position\", leader_pos)` ; note that writing is done asynchronously so it takes less time than reading.\n" + "- `dtWfoll: 0.22 (4446.9hz)` which is the number of milliseconds it took to set a new goal position for the follower arm using `follower_arm.write(\"Goal_position\", leader_pos)` ; note that writing is done asynchronously so it takes less time than reading.\n", + "\n", + "*Importantly: If you don't have any camera, you can remove them dynamically with this [draccus](https://github.com/dlwh/draccus) syntax `--robot.cameras='{}'`*:\n", + "```bash\n", + "python lerobot/scripts/control_robot.py \\\n", + " --robot.type=koch \\\n", + " --robot.cameras='{}' \\\n", + " --control.type=teleoperate\n", + "```\n", + "\n", + "*We advise to create a new yaml file when the command becomes too long.*" ] }, { diff --git a/packages/robots/lerobot/lerobot_overlay/notebooks/7-3_real-robot_record-and-visualize.ipynb b/packages/robots/lerobot/lerobot_overlay/notebooks/7-3_real-robot_record-and-visualize.ipynb index b9fc80b2c..f171c9e02 100644 --- a/packages/robots/lerobot/lerobot_overlay/notebooks/7-3_real-robot_record-and-visualize.ipynb +++ b/packages/robots/lerobot/lerobot_overlay/notebooks/7-3_real-robot_record-and-visualize.ipynb @@ -22,7 +22,39 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## a. Use `koch.yaml` and the `record` function" + "*Using what you've learned previously, you can now easily record a dataset of states and actions for one episode. You can use `busy_wait` to control the speed of teleoperation and record at a fixed `fps` (frame per seconds).*\n", + "\n", + "*Try this code to record 30 seconds at 60 fps:*\n", + "```python\n", + "import time\n", + "from lerobot.scripts.control_robot import busy_wait\n", + "\n", + "record_time_s = 30\n", + "fps = 60\n", + "\n", + "states = []\n", + "actions = []\n", + "for _ in range(record_time_s * fps):\n", + " start_time = time.perf_counter()\n", + " observation, action = robot.teleop_step(record_data=True)\n", + "\n", + " states.append(observation[\"observation.state\"])\n", + " actions.append(action[\"action\"])\n", + "\n", + " dt_s = time.perf_counter() - start_time\n", + " busy_wait(1 / fps - dt_s)\n", + "\n", + "# Note that observation and action are available in RAM, but\n", + "# you could potentially store them on disk with pickle/hdf5 or\n", + "# our optimized format `LeRobotDataset`. More on this next.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## a. Use the `record` function" ] }, { @@ -40,7 +72,7 @@ "HF_USER=$(huggingface-cli whoami | head -n 1)\n", "echo $HF_USER\n", "```\n", - "If you don't want to push to hub, use `--push-to-hub 0`.\n", + "If you don't want to push to hub, use `-control.push_to_hub=false`.\n", "\n", ":::" ] @@ -49,21 +81,57 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now run the following in the terminal to record 2 episodes:\n", + "*Now run the following in the terminal to record 2 episodes:*\n", "\n", "```\n", - "python lerobot/scripts/control_robot.py record \\\n", - " --robot-path lerobot/configs/robot/koch.yaml \\\n", - " --fps 30 \\\n", - " --root data \\\n", - " --repo-id ${HF_USER}/koch_test \\\n", - " --tags tutorial \\\n", - " --warmup-time-s 5 \\\n", - " --episode-time-s 30 \\\n", - " --reset-time-s 30 \\\n", - " --num-episodes 2 \\\n", - " --push-to-hub 0 \\\n", - " --run-compute-stats 1\n", + "python lerobot/scripts/control_robot.py \\\n", + " --robot.type=koch \\\n", + " --control.type=record \\\n", + " --control.single_task=\"Grasp a lego block and put it in the bin.\" \\\n", + " --control.fps=30 \\\n", + " --control.repo_id=${HF_USER}/koch_test \\\n", + " --control.tags='[\"tutorial\"]' \\\n", + " --control.warmup_time_s=5 \\\n", + " --control.episode_time_s=30 \\\n", + " --control.reset_time_s=30 \\\n", + " --control.num_episodes=2 \\\n", + " --control.push_to_hub=true\n", + "```\n", + "\n", + "*This will write your dataset locally to `~/.cache/huggingface/lerobot/{repo-id}` (e.g. `data/cadene/koch_test`) and push it on the hub at `https://huggingface.co/datasets/{HF_USER}/{repo-id}`. Your dataset will be automatically tagged with `LeRobot` for the community to find it easily, and you can also add custom tags (in this case `tutorial` for example).*\n", + "\n", + "*You can look for other LeRobot datasets on the hub by searching for `LeRobot` tags: https://huggingface.co/datasets?other=LeRobot*\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You will see a lot of lines appearing like this one:*\n", + "```\n", + "INFO 2024-08-10 15:02:58 ol_robot.py:219 dt:33.34 (30.0hz) dtRlead: 5.06 (197.5hz) dtWfoll: 0.25 (3963.7hz) dtRfoll: 6.22 (160.7hz) dtRlaptop: 32.57 (30.7hz) dtRphone: 33.84 (29.5hz)\n", + "```\n", + "*It contains:*\n", + "- `2024-08-10 15:02:58` which is the date and time of the call to the print function,\n", + "- `ol_robot.py:219` which is the end of the file name and the line number where the print function is called (`lerobot/scripts/control_robot.py` line `219`).\n", + "- `dt:33.34 (30.0hz)` which is the \"delta time\" or the number of milliseconds spent between the previous call to `robot.teleop_step(record_data=True)` and the current one, associated with the frequency (33.34 ms equals 30.0 Hz) ; note that we use `--fps 30` so we expect 30.0 Hz ; when a step takes more time, the line appears in yellow.\n", + "- `dtRlead: 5.06 (197.5hz)` which is the delta time of reading the present position of the leader arm.\n", + "- `dtWfoll: 0.25 (3963.7hz)` which is the delta time of writing the goal position on the follower arm ; writing is asynchronous so it takes less time than reading.\n", + "- `dtRfoll: 6.22 (160.7hz)` which is the delta time of reading the present position on the follower arm.\n", + "- `dtRlaptop:32.57 (30.7hz) ` which is the delta time of capturing an image from the laptop camera in the thread running asynchronously.\n", + "- `dtRphone:33.84 (29.5hz)` which is the delta time of capturing an image from the phone camera in the thread running asynchronously.\n", + "\n", + "*Troubleshooting:*\n", + "- On Linux, if you encounter any issue during video encoding with `ffmpeg: unknown encoder libsvtav1`, you can:\n", + " - install with conda-forge by running `conda install -c conda-forge ffmpeg` (it should be compiled with `libsvtav1`),\n", + " - or, install [Homebrew](https://brew.sh) and run `brew install ffmpeg` (it should be compiled with `libsvtav1`),\n", + " - or, install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1),\n", + " - and, make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.\n", + "- On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).~~\n", + "\n", + "*At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/koch_test) that you can obtain by running:*\n", + "```bash\n", + "echo https://huggingface.co/datasets/${HF_USER}/koch_test\n", "```" ] }, diff --git a/run.sh b/run.sh index 7804be779..00ae40f53 100755 --- a/run.sh +++ b/run.sh @@ -29,6 +29,9 @@ output_width="1280" output_height="720" output_fps="30" +# Default network setting +docker_network="host" + # Loop through arguments for arg in "$@"; do # Check for the --csi2webcam option @@ -154,12 +157,12 @@ if [[ "$csi_to_webcam_conversion" == true ]]; then echo "CSI Capture resolution: ${capture_width}x${capture_height}@${capture_fps}" echo "CSI Output resolution : ${output_width}x${output_height}@${output_fps}" - # Unset the DISPLAY env variable because, apparently, some GStreamer components might try to use this display + # Unset the DISPLAY env variable because, apparently, some GStreamer components might try to use this display # for video rendering or processing, which can conflict with other GStreamer elements or hardware device # Temporarily unset DISPLAY for the GStreamer command unset DISPLAY - + echo "gst-launch-1.0 -v nvarguscamerasrc sensor-id=${csi_index} \ ! 'video/x-raw(memory:NVMM), format=NV12, width=${capture_width}, height=${capture_height}, framerate=${capture_fps}/1' \ ! queue max-size-buffers=1 leaky=downstream ! nvvidconv \ @@ -178,7 +181,7 @@ if [[ "$csi_to_webcam_conversion" == true ]]; then ! multipartdemux single-stream=1 \ ! "image/jpeg, width=${output_width}, height=${output_height}, parsed=(boolean)true, colorimetry=(string)2:4:7:1, framerate=(fraction)${output_fps}/1, sof-marker=(int)0" \ ! v4l2sink device=${new_devices[$i]} sync=false > $ROOT/logs/gst-launch-process_${csi_index}.txt 2>&1 & - + # Store the PID of the background process if you want to manage it later BG_PIDS+=($!) echo "BG_PIDS: ${BG_PIDS[@]}" @@ -231,7 +234,7 @@ if [ -n "$DISPLAY" ]; then echo "### DISPLAY environmental variable is already set: \"$DISPLAY\"" # give docker root user X11 permissions xhost +si:localuser:root || sudo xhost +si:localuser:root - + # enable SSH X11 forwarding inside container (https://stackoverflow.com/q/48235040) XAUTH=/tmp/.docker.xauth xauth nlist $DISPLAY | sed -e 's/^..../ffff/' | xauth -f $XAUTH nmerge - @@ -284,7 +287,7 @@ for arg in "$@"; do if [[ "$arg" != "--csi2webcam" && "$arg" != --csi-capture-res=* && "$arg" != --csi-output-res=* ]]; then filtered_args+=("$arg") # Add to the new array if not the argument to remove fi - + if [[ "$arg" = "--name" || "$arg" = --name* ]]; then HAS_CONTAINER_NAME=1 fi @@ -299,6 +302,12 @@ if [ -z "$HAS_CONTAINER_NAME" ]; then CONTAINER_NAME_FLAGS="--name $CONTAINER_NAME" fi +# Override if DOCKER_NETWORK env var is set +if [ ! -z "$DOCKER_NETWORK" ]; then + echo "[run.sh] Overriding docker network with: $DOCKER_NETWORK" + docker_network="$DOCKER_NETWORK" +fi + # run the container ARCH=$(uname -i) @@ -334,7 +343,7 @@ elif [ $ARCH = "x86_64" ]; then ( set -x ; - $SUDO docker run --gpus all -it --rm --network=host \ + $SUDO docker run --env NVIDIA_DRIVER_CAPABILITIES=compute,utility,graphics --gpus all -it --rm --network="$docker_network" \ --shm-size=8g \ --ulimit memlock=-1 \ --ulimit stack=67108864 \