# Project export: MARC: Marker Actuated Robotic Controller This document was generated by HackStack to give an AI agent context about a hackathon project. Sections are labeled with their provenance; content marked as truncated was cut to keep this document small. ## Project metadata - Hackathon: Cal Hacks 12.0 - Tagline: Meet MARC, the robot that turns text prompts into crisp drawings. In minutes, MARC uses LLM & LinAlg-based framework to drive a robot arm, ready for any setting where ink, motion, & imagination meet. - Devpost: https://devpost.com/software/marc-marker-actuated-robotic-controller - GitHub: https://github.com/yyardi/cal-hacks-marc - Video: https://www.youtube.com/embed/qy0NNvkc4do?enablejsapi=1&hl=en_US&rel=0&start=&version=3&wmode=transparent - Result: winner (BitRobot Network: Best Robotics Hack - 2nd Place) - Team: 2 GitHub contributor(s) β€” Yash Yardi (53 commits), OrangePainting (1 commits) ## Devpost submission (written by the team) ### Inspiration Our inspiration behind MARC stems from wanting to close the gaps between imagination and creation. Today, turning a digital idea into something physical can take hours of calibration and setup, killing spontaneous creativity. High costs also make creative robotics inaccessible. For example, many precision robot arms cost over $10,000, putting them far beyond the reach of students, artists, and hobbyists. And perhaps most importantly, over 285 million people with visual impairments are excluded from visual creation tools; while AI-generated art continues to evolve, those who can’t see it are still unable to feel what it creates. Our project aims to change that by making the process of turning ideas into tangible, touchable art faster, cheaper, and more inclusive. ### What it does The system takes natural language text prompts and optional image references to generate original artwork, then automatically translates these digital creations into precise physical drawings executed by a robotic arm. Through advanced coordinate transformation and real-time motion control, MARC bridges the gap between AI-generated images and tangible art, delivering sub-2mm positioning accuracy as it brings digital designs to life on paper. ### How we built it The system architecture consists of four integrated stages. First, AI generation uses a Stable Diffusion XL pipeline with SD 1.5 fallback to create artwork from text prompts. Next, smart vectorization converts PNG outputs to SVG format using Portrace and SVGpathtools while fitting the artwork into 15 cm x 17.3 cm page dimensions. The third stage handles precise coordinate transformation by detecting paper corners via camera vision to build a homography matrix that maps pixels to millimeters, then applies 2D affine transformation formulas to convert page coordinates into the robot's base frame. Finally, robotics execution is achieved through a 6-DOF SO-ARM 101 with degrees-mode control, a custom 3-link planar IK solver, and real-time joint angle streaming for smooth, accurate drawing movements. ### Challenges we ran into We encountered three major technical challenges during development. First, inaccurate motors caused servo backlash, USB packet loss, and position error accumulation. We solved this by implementing a closed-loop inverse kinematics controller with real-time error correction, achieving sub-2mm accuracy. Second, integrating three frameworks with incompatible APIs (LeRobot using degrees, RustyPot using 0-1, and PySerial using bytes) cost us over 20 hours in rewrites. We built a thin abstraction layer under 200 lines, prototyped all three approaches in 6 hours, and selected LeRobot for its community support and degrees-native interface. Finally, marker physics issues like nib compression and inconsistent ink flow limited us to one marker shape. We designed a universal 3D-printed spring-loaded adapter with 5-18mm diameter compatibility that maintains consistent pressure while absorbing vibrations. ### Accomplishments we're proud of We're incredibly proud of building a system that accurately translates drawing coordinates into precise robotic movements, achieving pinpoint accuracy within 2mm. Figuring out how to convert real-world positions into instructions the robot could understand was a major breakthrough, requiring us to use camera vision to map the physical paper space to the robot's coordinate system. Beyond the technical work, we're proud of handling the pressure and rapid problem-solving of our first hackathon, transforming an ambitious idea into a working system that takes a text prompt and produces a physical drawing on paper in minutes. ### What we learned This project taught us invaluable lessons about collaboration and resourcefulness. We discovered the power of open-source communities, finding extensive documentation and ready-to-use tools like LeRobot that accelerated our development and saved us countless hours of building from scratch. We learned the importance of parallel experimentation, testing multiple approaches simultaneously rather than betting everything on a single solution, which proved crucial when we had to evaluate three different frameworks in just six hours. Most importantly, we realized that constant communication is essential in a fast-paced hackathon environment. Keeping everyone aligned on goals, progress, and challenges minimized confusion and prevented wasted effort from misunderstandings. Working under pressure taught us to balance ambitious technical goals with practical time constraints, and that success comes from both individual problem-solving and effective teamwork. ### What's next Looking ahead, we plan to expand MARC's capabilities in several exciting directions. First, we'll add multi-color support to enable more vibrant and complex artwork. We're committed to open-source publishing, making our code and designs available for others to build upon and learn from. To make MARC more accessible, we'll develop a remote control system with a queue feature, allowing multiple users to submit drawing requests from anywhere. We also envision an adaptive learning system that improves drawing quality over time by learning from past movements and corrections. Finally, we want to transform MARC into a multi-modal fabrication platform that goes beyond drawing, potentially supporting different tools and creative outputs to bridge the gap between digital creativity and physical making. ## README (from the GitHub repository)

LeRobot, Hugging Face Robotics Library

[![Tests](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml?query=branch%3Amain) [![Python versions](https://img.shields.io/pypi/pyversions/lerobot)](https://www.python.org/downloads/) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/huggingface/lerobot/blob/main/LICENSE) [![Status](https://img.shields.io/pypi/status/lerobot)](https://pypi.org/project/lerobot/) [![Version](https://img.shields.io/pypi/v/lerobot)](https://pypi.org/project/lerobot/) [![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.1-ff69b4.svg)](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md) [![Discord](https://dcbadge.vercel.app/api/server/C5P34WJ68S?style=flat)](https://discord.gg/s3KuuzsPFb)

Build Your Own HopeJR Robot!

HopeJR robot

Meet HopeJR – A humanoid robot arm and hand for dexterous manipulation!

Control it with exoskeletons and gloves for precise hand movements.

Perfect for advanced manipulation tasks! πŸ€–

See the full HopeJR tutorial here.


Build Your Own SO-101 Robot!

SO-101 follower arm SO-101 leader arm

Meet the updated SO100, the SO-101 – Just €114 per arm!

Train it in minutes with a few simple moves on your laptop.

Then sit back and watch your creation act autonomously! 🀯

See the full SO-101 tutorial here.

Want to take it to the next level? Make your SO-101 mobile by building LeKiwi!

Check out the LeKiwi tutorial and bring your robot to life on wheels.

LeKiwi mobile robot

LeRobot: State-of-the-art AI for real-world robotics

--- πŸ€— LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier to entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models. πŸ€— LeRobot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning. πŸ€— LeRobot already provides a set of pretrained models, datasets with human collected demonstrations, and simulation environments to get started without assembling a robot. In the coming weeks, the plan is to add more and more support for real-world robotics on the most affordable and capable robots out there. πŸ€— LeRobot hosts pretrained models and datasets on this Hugging Face community page: [huggingface.co/lerobot](https://huggingface.co/lerobot) #### Examples of pretrained models on simulation environments
ACT policy on ALOHA env TDMPC policy on SimXArm env Diffusion policy on PushT env
ACT policy on ALOHA env TDMPC policy on SimXArm env Diffusion policy on PushT env
## Installation LeRobot works with Python 3.10+ and PyTorch 2.2+. ### Environment Setup Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniforge`](https://conda-forge.org/download/): ```bash conda create -y -n lerobot python=3.10 conda activate lerobot ``` When using `conda`, install `ffmpeg` in your environment: ```bash conda install ffmpeg -c conda-forge ``` > **NOTE:** This usually installs `ffmpeg 7.X` for your platform compiled with the `libsvtav1` encoder. If `libsvtav1` is not supported (check supported encoders with `ffmpeg -encoders`), you can: > > - _[On any platform]_ Explicitly install `ffmpeg 7.X` using: > > ```bash > conda install ffmpeg=7.1.1 -c conda-forge > ``` > > - _[On Linux only]_ 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), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`. ### Install LeRobot πŸ€— #### From Source First, clone the repository and navigate into the directory: ```bash git clone https://github.com/huggingface/lerobot.git cd lerobot ``` Then, install the library in editable mode. This is useful if you plan to contribute to the code. ```bash pip install -e . ``` > **NOTE:** If you encounter build errors, you may need to install additional dependencies (`cmake`, `build-essential`, and `ffmpeg libs`). On Linux, run: > `sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev`. For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg) For simulations, πŸ€— LeRobot comes with gymnasium environments that can be installed as extras: - [aloha](https://github.com/huggingface/gym-aloha) - [xarm](https://github.com/huggingface/gym-xarm) - [pusht](https://github.com/huggingface/gym-pusht) For instance, to install πŸ€— LeRobot with aloha and pusht, use: ```bash pip install -e ".[aloha, pusht]" ``` ### Installation from PyPI **Core Library:** Install the base package with: ```bash pip install lerobot ``` _This installs only the default dependencies._ **Extra Features:** To install additional functionality, use one of the following: ```bash pip install 'lerobot[all]' # All available features pip install 'lerobot[aloha,pusht]' # Specific features (Aloha & Pusht) pip install 'lerobot[feetech]' # Feetech motor support ``` _Replace `[...]` with your desired features._ **Available Tags:** For a full list of optional dependencies, see: https://pypi.org/project/lerobot/ ### Weights & Biases To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with ```bash wandb login ``` (note: you will also need to enable WandB in the configuration. See below.) ### Visualize datasets Check out [exa [README truncated for size] ## Detected evidence (automated analysis) Indexed codebase: 405 recognized source files, 3765 KB. - Hugging Face (technology) β€” detected in the code - Python (language) β€” detected in the code - PyTorch (technology) β€” detected in the code - AI coding agent: GitHub Copilot β€” evidence: commit authorship or trailers ## Codebase structure (from repository index) ### Files (120 of 500) ``` .dockerignore .gitattributes .github/ISSUE_TEMPLATE/bug-report.yml .github/PULL_REQUEST_TEMPLATE.md .github/workflows/documentation-upload-pr.yml .github/workflows/documentation.yml .github/workflows/fast_tests.yml .github/workflows/full_tests.yml .github/workflows/nightly.yml .github/workflows/quality.yml .github/workflows/release.yml .github/workflows/security.yml .github/workflows/stale.yml .github/workflows/unbound_deps_tests.yml .gitignore .pre-commit-config.yaml benchmarks/video/benchmark.py benchmarks/video/capture_camera_feed.py benchmarks/video/README.md benchmarks/video/run_video_benchmark.py CODE_OF_CONDUCT.md CONTRIBUTING.md docker/Dockerfile.internal docker/Dockerfile.user docs-requirements.txt docs/README.md docs/source/_toctree.yml docs/source/act.mdx docs/source/async.mdx docs/source/backwardcomp.mdx docs/source/cameras.mdx docs/source/contributing.md docs/source/debug_processor_pipeline.mdx docs/source/feetech.mdx docs/source/hilserl_sim.mdx docs/source/hilserl.mdx docs/source/hope_jr.mdx docs/source/il_robots.mdx docs/source/il_sim.mdx docs/source/implement_your_own_processor.mdx docs/source/index.mdx docs/source/installation.mdx docs/source/integrate_hardware.mdx docs/source/introduction_processors.mdx docs/source/koch.mdx docs/source/lekiwi.mdx docs/source/lerobot-dataset-v3.mdx docs/source/libero.mdx docs/source/metaworld.mdx docs/source/multi_gpu_training.mdx docs/source/notebooks.mdx docs/source/phone_teleop.mdx docs/source/pi0.mdx docs/source/pi05.mdx docs/source/policy_act_README.md docs/source/policy_diffusion_README.md docs/source/policy_smolvla_README.md docs/source/policy_tdmpc_README.md docs/source/policy_vqbet_README.md docs/source/porting_datasets_v3.mdx docs/source/processors_robots_teleop.mdx docs/source/reachy2.mdx docs/source/smolvla.mdx docs/source/so100.mdx docs/source/so101.mdx docs/source/using_dataset_tools.mdx examples/backward_compatibility/replay.py examples/dataset/load_lerobot_dataset.py examples/dataset/use_dataset_image_transforms.py examples/dataset/use_dataset_tools.py examples/lekiwi/archived/krish_so101_controller.py examples/lekiwi/archived/paintbot_examples.py examples/lekiwi/archived/simple_svg_draw.py examples/lekiwi/archived/slower_so101_yash_controller_3.py examples/lekiwi/archived/svg_path_loading.py examples/lekiwi/archived/svg_to_robot.py examples/lekiwi/evaluate.py examples/lekiwi/paint_bot_examples_improved.py examples/lekiwi/record.py examples/lekiwi/replay.py examples/lekiwi/teleoperate.py examples/marc/calib/__init__.py examples/marc/calib/detect_tags.py examples/marc/calib/estimate_homography.py examples/marc/calib/homography.py examples/marc/calib/page_to_robot.py examples/marc/calib/warp.py examples/marc/constants.py examples/marc/executor/__init__.py examples/marc/executor/driver_api.py examples/marc/executor/so100_driver.py examples/marc/fetch_so101_urdf.py examples/marc/planner/__init__.py examples/marc/planner/color_quant.py examples/marc/planner/make_plan.py examples/marc/planner/order_paths.py examples/marc/planner/sample_curves.py examples/marc/planner/vector_planning.py examples/marc/README.md examples/marc/requirements.txt examples/marc/run_draw_lerobot_ik.py examples/marc/run_draw_test_square.py examples/marc/run_prompt_to_robot.py examples/marc/run_svg.py examples/marc/vectorize/__init__.py examples/marc/vectorize/generate.py examples/marc/vectorize/potrace_wrap.py examples/marc/vectorize/simplify_svg.py examples/phone_to_so100/evaluate.py examples/phone_to_so100/record.py examples/phone_to_so100/replay.py examples/phone_to_so100/teleoperate.py examples/port_datasets/display_error_files.py examples/port_datasets/port_droid.py examples/port_datasets/slurm_aggregate_shards.py examples/port_datasets/slurm_port_shards.py examples/port_datasets/slurm_upload.py examples/so100_to_so100_EE/evaluate.py examples/so100_to_so100_EE/record.py examples/so100_to_so100_EE/replay.py [380 more files omitted for size] ``` ### Dependencies - examples/marc/requirements.txt: accelerate, cairosvg, diffusers, numpy, opencv-contrib-python, pillow, pupil-apriltags, svgpathtools, transformers, vpype - pyproject.toml: accelerate@>=1.7.0,<2.0.0, accelerate@>=1.10.0,<2.0.0, av@>=15.0.0,<16.0.0, cmake@>=3.29.0.1,<4.2.0, datasets@>=4.0.0,<4.2.0, debugpy@>=1.8.1,<1.9.0, deepdiff@>=7.0.1,<9.0.0, diffusers@>=0.27.2,<0.36.0, draccus@==0.10.0, dynamixel-sdk@>=3.7.31,<3.9.0, einops@>=0.8.0,<0.9.0, feetech-servo-sdk@>=1.0.0,<2.0.0, grpcio@==1.73.1, grpcio-tools@==1.73.1, gym-aloha@>=0.1.2,<0.2.0, gym-hil@>=0.1.13,<0.2.0, gym-pusht@>=0.1.5,<0.2.0, gymnasium@>=1.1.1,<2.0.0, hebi-py@>=2.8.0,<2.12.0, hidapi@>=0.14.0,<0.15.0, huggingface-hub[hf-transfer,cli]@>=0.34.2,<0.36.0, imageio[ffmpeg]@>=2.34.0,<3.0.0, jsonlines@>=4.0.0,<5.0.0, lerobot[aloha], lerobot[async], lerobot[dev], lerobot[dynamixel], lerobot[feetech], lerobot[feetech], lerobot[gamepad], lerobot[grpcio-dep], lerobot[grpcio-dep], lerobot[grpcio-dep], lerobot[hilserl], lerobot[hopejr], lerobot[intelrealsense], lerobot[kinematics], lerobot[lekiwi], lerobot[libero], lerobot[metaworld], lerobot[phone], lerobot[pi], lerobot[placo-dep], lerobot[placo-dep], lerobot[pusht], lerobot[pygame-dep], lerobot[pygame-dep], lerobot[reachy2], lerobot[smolvla], lerobot[test], lerobot[transformers-dep], lerobot[transformers-dep], lerobot[transformers-dep], lerobot[video_benchmark], libero@@ git+https://github.com/huggingface/lerobot-libero.git@main#egg=libero, matplotlib@>=3.10.3,<4.0.0, metaworld@==3.0.0, mock-serial@>=0.0.1,<0.1.0, num2words@>=0.5.14,<0.6.0, opencv-python-headless@>=4.9.0,<4.13.0, packaging@>=24.2,<26.0, pandas@>=2.2.2,<2.4.0, placo@>=0.9.6,<0.10.0, pre-commit@>=3.7.0,<5.0.0, protobuf@==6.31.0, pygame@>=2.5.1,<2.7.0, pymunk@>=6.6.0,<7.0.0, pynput@>=1.7.7,<1.9.0, pyrealsense2@>=2.55.1.6486,<2.57.0, pyrealsense2-macosx@>=2.54,<2.55.0, pyserial@>=3.5,<4.0, pytest@>=8.1.0,<9.0.0, pytest-cov@>=5.0.0,<8.0.0, pytest-timeout@>=2.4.0,<3.0.0, pyzmq@>=26.2.1,<28.0.0, reachy2_sdk@>=1.0.14,<1.1.0, rerun-sdk@>=0.24.0,<0.27.0, safetensors@>=0.4.3,<1.0.0, scikit-image@>=0.23.2,<0.26.0, setuptools@>=71.0.0,<81.0.0, teleop@>=0.1.0,<0.2.0, termcolor@>=2.4.0,<4.0.0, torch@>=2.2.1,<2.8.0, torchcodec@>=0.2.1,<0.6.0, torchvision@>=0.21.0,<0.23.0, transformers@>=4.53.0,<5.0.0, transformers@@ git+https://github.com/huggingface/transformers.git@fix/lerobot_openpi, wandb@>=0.20.0,<0.22.0 ### Recent commits (newest first) - Merge pull request #27 from yyardi/codex/fix-inverse-kinematics-for-paint_bot_examples_improved-ubur49 - Merge branch 'main' into codex/fix-inverse-kinematics-for-paint_bot_examples_improved-ubur49 - Allow paint bot star command to auto-download URDF - Merge pull request #26 from yyardi/codex/fix-inverse-kinematics-for-paint_bot_examples_improved - Improve paint bot IK robustness and add drawing CLI - Merge pull request #25 from yyardi/codex/fix-svg-loading-and-drawing-placement - Merge branch 'main' into codex/fix-svg-loading-and-drawing-placement - Restore automatic follower calibration - Revert "Krish Konda Added Rudimentary Drawing" - Merge pull request #24 from yyardi/codex/fix-svg-loading-and-drawing-placement - Merge branch 'main' into codex/fix-svg-loading-and-drawing-placement - Auto-calibrate SO100 follower when offsets missing - Merge pull request #23 from yyardi/codex/fix-svg-loading-and-drawing-placement-lh10ve - Merge branch 'main' into codex/fix-svg-loading-and-drawing-placement-lh10ve - Ensure SO101 downloader grabs mesh assets - Merge pull request #22 from yyardi/codex/fix-svg-loading-and-drawing-placement - Add helper to download the SO101 URDF - Merge pull request #21 from yyardi/codex/fix-svg-loading-and-drawing-placement-n3vsmj - Merge branch 'main' into codex/fix-svg-loading-and-drawing-placement-n3vsmj - Allow reusing prompt-to-robot intermediates ## Key source files (fetched from GitHub, selected and truncated for size) ### CODE_OF_CONDUCT.md ```markdown # Contributor Covenant Code of Conduct ## Our Pledge We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, or sexual identity and orientation. We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community. ## Our Standards Examples of behavior that contributes to a positive environment for our community include: - Demonstrating empathy and kindness toward other people - Being respectful of differing opinions, viewpoints, and experiences - Giving and gracefully accepting constructive feedback - Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience - Focusing on what is best not just for us as individuals, but for the overall community Examples of unacceptable behavior include: - The use of sexualized language or imagery, and sexual attention or advances of any kind - Trolling, insulting or derogatory comments, and personal or political attacks - Public or private harassment - Publishing others' private information, such as a physical or email address, without their explicit permission - Other conduct which could reasonably be considered inappropriate in a professional setting ## Enforcement Responsibilities Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful. Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate. ## Scope This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official email address, posting via an official social media account, or acting as an appointed representative at an online or offline event. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at [feedback@huggingface.co](mailto:feedback@huggingface.co). All complaints will be reviewed and investigated promptly and fairly. All community leaders are obligated to respect the privacy and security of the reporter of any incident. ## Enforcement Guidelines Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in vi [truncated β€” 2521 more characters] ``` ### CONTRIBUTING.md ```markdown # How to contribute to πŸ€— LeRobot? Everyone is welcome to contribute, and we value everybody's contribution. Code is thus not the only way to help the community. Answering questions, helping others, reaching out and improving the documentations are immensely valuable to the community. It also helps us if you spread the word: reference the library from blog posts on the awesome projects it made possible, shout out on Twitter when it has helped you, or simply ⭐️ the repo to say "thank you". Whichever way you choose to contribute, please be mindful to respect our [code of conduct](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md). ## You can contribute in so many ways! Some of the ways you can contribute to πŸ€— LeRobot: - Fixing outstanding issues with the existing code. - Implementing new models, datasets or simulation environments. - Contributing to the examples or to the documentation. - Submitting issues related to bugs or desired new features. Following the guides below, feel free to open issues and PRs and to coordinate your efforts with the community on our [Discord Channel](https://discord.gg/VjFz58wn3R). For specific inquiries, reach out to [Remi Cadene](mailto:remi.cadene@huggingface.co). If you are not sure how to contribute or want to know the next features we working on, look on this project page: [LeRobot TODO](https://github.com/orgs/huggingface/projects/46) ## Submitting a new issue or feature request Do your best to follow these guidelines when submitting an issue or a feature request. It will make it easier for us to come back to you quickly and with good feedback. ### Did you find a bug? The πŸ€— LeRobot library is robust and reliable thanks to the users who notify us of the problems they encounter. So thank you for reporting an issue. First, we would really appreciate it if you could **make sure the bug was not already reported** (use the search bar on Github under Issues). Did not find it? :( So we can act quickly on it, please follow these steps: - Include your **OS type and version**, the versions of **Python** and **PyTorch**. - A short, self-contained, code snippet that allows us to reproduce the bug in less than 30s. - The full traceback if an exception is raised. - Attach any other additional information, like screenshots, you think may help. ### Do you want a new feature? A good feature request addresses the following points: 1. Motivation first: - Is it related to a problem/frustration with the library? If so, please explain why. Providing a code snippet that demonstrates the problem is best. - Is it related to something you would need for a project? We'd love to hear about it! - Is it something you worked on and think could benefit the community? Awesome! Tell us what problem it solved for you. 2. Write a _paragraph_ describing the feature. 3. Provide a **code snippet** that demonstrates its future use. 4. In case this is related to a paper, please attach a link. 5. Attach any [truncated β€” 8314 more characters] ``` ### pyproject.toml ``` # 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. [build-system] requires = ["setuptools"] build-backend = "setuptools.build_meta" [project.urls] homepage = "https://huggingface.co/lerobot" documentation = "https://huggingface.co/docs/lerobot/index" source = "https://github.com/huggingface/lerobot" issues = "https://github.com/huggingface/lerobot/issues" discord = "https://discord.gg/s3KuuzsPFb" [project] name = "lerobot" version = "0.3.4" description = "πŸ€— LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch" readme = "README.md" license = { text = "Apache-2.0" } requires-python = ">=3.10" authors = [ { name = "RΓ©mi CadΓ¨ne", email = "re.cadene@gmail.com" }, { name = "Simon Alibert", email = "alibert.sim@gmail.com" }, { name = "Alexander Soare", email = "alexander.soare159@gmail.com" }, { name = "Quentin GallouΓ©dec", email = "quentin.gallouedec@ec-lyon.fr" }, { name = "Steven Palma", email = "imstevenpmwork@ieee.org" }, { name = "Pepijn Kooijmans", email = "pepijnkooijmans@outlook.com"}, { name = "Michel Aractingi", email = "michel.aractingi@gmail.com"}, { name = "Adil Zouitine", email = "adilzouitinegm@gmail.com" }, { name = "Dana Aubakirova", email = "danaaubakirova17@gmail.com"}, { name = "Caroline Pascal", email = "caroline8.pascal@gmail.com"}, { name = "Martino Russi", email = "nopyeps@gmail.com"}, { name = "Thomas Wolf", email = "thomaswolfcontact@gmail.com" }, ] classifiers = [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3.10", "Topic :: Software Development :: Build Tools", "Topic :: Scientific/Engineering :: Artificial Intelligence", ] keywords = ["lerobot", "huggingface", "robotics", "machine learning", "artificial intelligence"] dependencies = [ # Hugging Face dependencies "datasets>=4.0.0,<4.2.0", "diffusers>=0.27.2,<0.36.0", "huggingface-hub[hf-transfer,cli]>=0.34.2,<0.36.0", "accelerate>=1.10.0,<2.0.0", # Core dependencies "setuptools>=71.0.0,<81.0.0", "cmake>=3.29.0.1,<4.2.0", "einops>=0.8.0,<0.9.0", "opencv-python-headless>=4.9.0,<4.13.0", "av>=15.0.0,<16.0.0", "jsonlines>=4.0.0,<5.0.0", "packaging>=24.2,<26.0", "pynput>=1.7.7,<1.9.0", "pyserial>=3.5,<4.0", "wandb>=0.20.0,<0.22.0", # TODO: Bumb dependency (compatible with protobuf) "torch>=2.2.1,<2.8.0", # TODO: Bumb dependency "torchcodec>=0.2.1,<0.6.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')", # TODO: Bumb dependency "torchvision>=0.21.0,<0.23.0", # TODO: Bumb dependency "draccus==0.10.0", # TODO: Remove == "gymnasium>=1.1.1,<2.0.0", "rerun-sdk>=0.24.0,<0.27.0", # Support dependencies "deepdiff>=7.0.1,<9.0.0", "imageio[ffmpeg]>=2.34.0,<3.0.0", "termcolor>=2.4.0,<4.0.0", ] # Optional dependencies [project.optional-dependencies] # Common pygame-dep = ["pygame>=2.5.1,<2.7.0"] placo-dep = ["placo>=0.9.6,<0.10.0"] transformers-dep = ["transformers>=4.53.0,<5.0.0"] grpcio-dep = ["grpcio==1.73.1", "protobuf==6.31.0"] # TODO: Bumb dependency (compatible with wandb) # Motors feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0"] dynamixel = ["dynamixel-sdk>=3.7.31,<3.9.0"] # Robots gamepad = ["lerobot[pygame-dep]", "hidapi>=0.14.0,<0.15.0"] hopejr = ["lerobot[feetech]", "lerobot[pygame-dep]"] lekiwi = ["lerobot[feetech]", "pyzmq>=26.2.1,<28.0.0"] reachy2 = ["reachy2_sdk>=1.0.14,<1.1.0"] kinematics = ["lerobot[placo-dep]"] intelrealsense = [ "pyrealsense2>=2.55.1.6486,<2.57.0 ; sys_platform != 'darwin'", "pyrealsense2-macosx>=2.54,<2.55.0 ; sys_platform == 'darwin'", ] phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0"] # Policies pi = ["transformers @ git+https://github.com/huggingface/transformers.git@fix/lerobot_openpi"] smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14,<0.6.0", "accelerate>=1.7.0,<2.0.0", "safetensors>=0.4.3,<1.0.0"] hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"] # Features async = ["lerobot[grpcio-dep]", "matplotlib>=3.10.3,<4.0.0"] # Development dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1"] test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"] video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"] # Simulation aloha = ["gym-aloha>=0.1.2,<0.2.0"] pusht = ["gym-pusht>=0.1.5,<0.2.0", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead libero = ["lerobot[transformers-dep]", "libero @ git+https://github.com/huggingface/lerobot-libero.git@main#egg=libero"] metaworld = ["metaworld==3.0.0"] # All all = [ "lerobot[dynamixel]", "lerobot[gamepad]", "lerobot[hopejr]", "lerobot[lekiwi]", "lerobot[reachy2]", "lerobot[kinematics]", "lerobot[intelrealsense]", "lerobot[pi]", "lerobot[smolvla]", "lerobot[hilserl]", "lerobot[async]", "lerobot[dev]", "lerobot[test]", "le [truncated β€” 5264 more characters] ``` ### examples/marc/requirements.txt ``` diffusers transformers accelerate opencv-contrib-python numpy pillow pupil-apriltags vpype cairosvg svgpathtools ``` ### .pre-commit-config.yaml ```yaml # 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. default_language_version: python: python3.10 exclude: "tests/artifacts/.*\\.safetensors$" repos: ##### Meta ##### - repo: meta hooks: - id: check-useless-excludes - id: check-hooks-apply ##### General Code Quality & Formatting ##### - repo: https://github.com/pre-commit/pre-commit-hooks rev: v6.0.0 hooks: - id: check-added-large-files args: ['--maxkb=1024'] - id: debug-statements - id: check-merge-conflict - id: check-case-conflict - id: check-yaml - id: check-toml - id: end-of-file-fixer - id: trailing-whitespace - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.14.1 hooks: - id: ruff-format - id: ruff args: [--fix, --exit-non-zero-on-fix] - repo: https://github.com/adhtruong/mirrors-typos rev: v1.38.1 hooks: - id: typos args: [--force-exclude] - repo: https://github.com/asottile/pyupgrade rev: v3.21.0 hooks: - id: pyupgrade args: [--py310-plus] ##### Markdown Quality ##### - repo: https://github.com/rbubley/mirrors-prettier rev: v3.6.2 hooks: - id: prettier name: Format Markdown with Prettier types_or: [markdown, mdx] args: [--prose-wrap=preserve] ##### Security ##### - repo: https://github.com/gitleaks/gitleaks rev: v8.28.0 hooks: - id: gitleaks - repo: https://github.com/woodruffw/zizmor-pre-commit rev: v1.15.2 hooks: - id: zizmor - repo: https://github.com/PyCQA/bandit rev: 1.8.6 hooks: - id: bandit args: ["-c", "pyproject.toml"] additional_dependencies: ["bandit[toml]"] # TODO(Steven): Uncomment when ready to use ##### Static Analysis & Typing ##### - repo: https://github.com/pre-commit/mirrors-mypy rev: v1.18.2 hooks: - id: mypy args: [--config-file=pyproject.toml] exclude: ^(examples|benchmarks|tests)/ ##### Docstring Checks ##### # - repo: https://github.com/akaihola/darglint2 # rev: v1.8.2 # hooks: # - id: darglint2 # args: ["--docstring-style", "google", "-v", "2"] # exclude: ^tests/.*$ # - repo: https://github.com/econchick/interrogate # rev: 1.7.0 # hooks: # - id: interrogate # args: ["-vv", "--config=pyproject.toml"] ``` ### tests/__init__.py ```python # 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. ``` ### tests/test_available.py ```python #!/usr/bin/env python # 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. import importlib import gymnasium as gym import pytest import lerobot from lerobot.policies.act.modeling_act import ACTPolicy from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy from lerobot.policies.tdmpc.modeling_tdmpc import TDMPCPolicy from lerobot.policies.vqbet.modeling_vqbet import VQBeTPolicy from tests.utils import require_env @pytest.mark.parametrize("env_name, task_name", lerobot.env_task_pairs) @require_env def test_available_env_task(env_name: str, task_name: list): """ This test verifies that all environments listed in `lerobot/__init__.py` can be successfully imported β€” if they're installed β€” and that their `available_tasks_per_env` are valid. """ package_name = f"gym_{env_name}" importlib.import_module(package_name) gym_handle = f"{package_name}/{task_name}" assert gym_handle in gym.envs.registry, gym_handle def test_available_policies(): """ This test verifies that the class attribute `name` for all policies is consistent with those listed in `lerobot/__init__.py`. """ policy_classes = [ACTPolicy, DiffusionPolicy, TDMPCPolicy, VQBeTPolicy] policies = [pol_cls.name for pol_cls in policy_classes] assert set(policies) == set(lerobot.available_policies), policies def test_print(): print(lerobot.available_envs) print(lerobot.available_tasks_per_env) print(lerobot.available_datasets) print(lerobot.available_datasets_per_env) print(lerobot.available_real_world_datasets) print(lerobot.available_policies) print(lerobot.available_policies_per_env) ``` ### tests/conftest.py ```python #!/usr/bin/env python # 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. import traceback import pytest from serial import SerialException from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature from tests.utils import DEVICE # Import fixture modules as plugins pytest_plugins = [ "tests.fixtures.dataset_factories", "tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.optimizers", "tests.plugins.reachy2_sdk", ] def pytest_collection_finish(): print(f"\nTesting with {DEVICE=}") def _check_component_availability(component_type, available_components, make_component): """Generic helper to check if a hardware component is available""" if component_type not in available_components: raise ValueError( f"The {component_type} type is not valid. Expected one of these '{available_components}'" ) try: component = make_component(component_type) component.connect() del component return True except Exception as e: print(f"\nA {component_type} is not available.") if isinstance(e, ModuleNotFoundError): print(f"\nInstall module '{e.name}'") elif isinstance(e, SerialException): print("\nNo physical device detected.") elif isinstance(e, ValueError) and "camera_index" in str(e): print("\nNo physical camera detected.") else: traceback.print_exc() return False @pytest.fixture def patch_builtins_input(monkeypatch): def print_text(text=None): if text is not None: print(text) monkeypatch.setattr("builtins.input", print_text) @pytest.fixture def policy_feature_factory(): """PolicyFeature factory""" def _pf(ft: FeatureType, shape: tuple[int, ...]) -> PolicyFeature: return PolicyFeature(type=ft, shape=shape) return _pf def assert_contract_is_typed(features: dict[PipelineFeatureType, dict[str, PolicyFeature]]) -> None: assert isinstance(features, dict) assert all(isinstance(k, PipelineFeatureType) for k in features) assert all(isinstance(v, dict) for v in features.values()) assert all(all(isinstance(nk, str) for nk in v) for v in features.values()) assert all(all(isinstance(nv, PolicyFeature) for nv in v.values()) for v in features.values()) ``` ### tests/test_control_robot.py ```python #!/usr/bin/env python # Copyright 2025 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. from unittest.mock import patch from lerobot.scripts.lerobot_calibrate import CalibrateConfig, calibrate from lerobot.scripts.lerobot_record import DatasetRecordConfig, RecordConfig, record from lerobot.scripts.lerobot_replay import DatasetReplayConfig, ReplayConfig, replay from lerobot.scripts.lerobot_teleoperate import TeleoperateConfig, teleoperate from tests.fixtures.constants import DUMMY_REPO_ID from tests.mocks.mock_robot import MockRobotConfig from tests.mocks.mock_teleop import MockTeleopConfig def test_calibrate(): robot_cfg = MockRobotConfig() cfg = CalibrateConfig(robot=robot_cfg) calibrate(cfg) def test_teleoperate(): robot_cfg = MockRobotConfig() teleop_cfg = MockTeleopConfig() cfg = TeleoperateConfig( robot=robot_cfg, teleop=teleop_cfg, teleop_time_s=0.1, ) teleoperate(cfg) def test_record_and_resume(tmp_path): robot_cfg = MockRobotConfig() teleop_cfg = MockTeleopConfig() dataset_cfg = DatasetRecordConfig( repo_id=DUMMY_REPO_ID, single_task="Dummy task", root=tmp_path / "record", num_episodes=1, episode_time_s=0.1, reset_time_s=0, push_to_hub=False, ) cfg = RecordConfig( robot=robot_cfg, dataset=dataset_cfg, teleop=teleop_cfg, play_sounds=False, ) dataset = record(cfg) assert dataset.fps == 30 assert dataset.meta.total_episodes == dataset.num_episodes == 1 assert dataset.meta.total_frames == dataset.num_frames == 3 assert dataset.meta.total_tasks == 1 cfg.resume = True # Mock the revision to prevent Hub calls during resume with ( patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, ): mock_get_safe_version.return_value = "v3.0" mock_snapshot_download.return_value = str(tmp_path / "record") dataset = record(cfg) assert dataset.meta.total_episodes == dataset.num_episodes == 2 assert dataset.meta.total_frames == dataset.num_frames == 6 assert dataset.meta.total_tasks == 1 def test_record_and_replay(tmp_path): robot_cfg = MockRobotConfig() teleop_cfg = MockTeleopConfig() record_dataset_cfg = DatasetRecordConfig( repo_id=DUMMY_REPO_ID, single_task="Dummy task", root=tmp_path / "record_and_replay", num_episodes=1, episode_time_s=0.1, push_to_hub=False, ) record_cfg = RecordConfig( robot=robot_cfg, dataset=record_dataset_cfg, teleop=teleop_cfg, play_sounds=False, ) replay_dataset_cfg = DatasetReplayConfig( repo_id=DUMMY_REPO_ID, episode=0, root=tmp_path / "record_and_replay", ) replay_cfg = ReplayConfig( robot=robot_cfg, dataset=replay_dataset_cfg, play_sounds=False, ) record(record_cfg) # Mock the revision to prevent Hub calls during replay with ( patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, ): mock_get_safe_version.return_value = "v3.0" mock_snapshot_download.return_value = str(tmp_path / "record_and_replay") replay(replay_cfg) ``` ### tests/utils.py ```python #!/usr/bin/env python # 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. import os import platform from functools import wraps import pytest import torch from lerobot import available_cameras, available_motors, available_robots from lerobot.utils.import_utils import is_package_available DEVICE = os.environ.get("LEROBOT_TEST_DEVICE", "cuda") if torch.cuda.is_available() else "cpu" TEST_ROBOT_TYPES = [] for robot_type in available_robots: TEST_ROBOT_TYPES += [(robot_type, True), (robot_type, False)] TEST_CAMERA_TYPES = [] for camera_type in available_cameras: TEST_CAMERA_TYPES += [(camera_type, True), (camera_type, False)] TEST_MOTOR_TYPES = [] for motor_type in available_motors: TEST_MOTOR_TYPES += [(motor_type, True), (motor_type, False)] # Camera indices used for connecting physical cameras OPENCV_CAMERA_INDEX = int(os.environ.get("LEROBOT_TEST_OPENCV_CAMERA_INDEX", 0)) INTELREALSENSE_SERIAL_NUMBER = int(os.environ.get("LEROBOT_TEST_INTELREALSENSE_SERIAL_NUMBER", 128422271614)) DYNAMIXEL_PORT = os.environ.get("LEROBOT_TEST_DYNAMIXEL_PORT", "/dev/tty.usbmodem575E0032081") DYNAMIXEL_MOTORS = { "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"], } FEETECH_PORT = os.environ.get("LEROBOT_TEST_FEETECH_PORT", "/dev/tty.usbmodem585A0080971") FEETECH_MOTORS = { "shoulder_pan": [1, "sts3215"], "shoulder_lift": [2, "sts3215"], "elbow_flex": [3, "sts3215"], "wrist_flex": [4, "sts3215"], "wrist_roll": [5, "sts3215"], "gripper": [6, "sts3215"], } def require_x86_64_kernel(func): """ Decorator that skips the test if plateform device is not an x86_64 cpu. """ from functools import wraps @wraps(func) def wrapper(*args, **kwargs): if platform.machine() != "x86_64": pytest.skip("requires x86_64 plateform") return func(*args, **kwargs) return wrapper def require_cpu(func): """ Decorator that skips the test if device is not cpu. """ from functools import wraps @wraps(func) def wrapper(*args, **kwargs): if DEVICE != "cpu": pytest.skip("requires cpu") return func(*args, **kwargs) return wrapper def require_cuda(func): """ Decorator that skips the test if cuda is not available. """ from functools import wraps @wraps(func) def wrapper(*args, **kwargs): if not torch.cuda.is_available(): pytest.skip("requires cuda") return func(*args, **kwargs) return wrapper def require_env(func): """ Decorator that skips the test if the required environment package is not installed. As it need 'env_name' in args, it also checks whether it is provided as an argument. If 'env_name' is None, this check is skipped. """ @wraps(func) def wrapper(*args, **kwargs): # Determine if 'env_name' is provided and extract its value arg_names = func.__code__.co_varnames[: func.__code__.co_argcount] if "env_name" in arg_names: # Get the index of 'env_name' and retrieve the value from args index = arg_names.index("env_name") env_name = args[index] if len(args) > index else kwargs.get("env_name") else: raise ValueError("Function does not have 'env_name' as an argument.") # Perform the package check package_name = f"gym_{env_name}" if env_name is not None and not is_package_available(package_name): pytest.skip(f"gym-{env_name} not installed") return func(*args, **kwargs) return wrapper def require_package_arg(func): """ Decorator that skips the test if the required package is not installed. This is similar to `require_env` but more general in that it can check any package (not just environments). As it need 'required_packages' in args, it also checks whether it is provided as an argument. If 'required_packages' is None, this check is skipped. """ @wraps(func) def wrapper(*args, **kwargs): # Determine if 'required_packages' is provided and extract its value arg_names = func.__code__.co_varnames[: func.__code__.co_argcount] if "required_packages" in arg_names: # Get the index of 'required_packages' and retrieve the value from args index = arg_names.index("required_packages") required_packages = args[index] if len(args) > index else kwargs.get("required_packages") else: raise ValueError("Function does not have 'required_packages' as an argument.") if required_packages is None: return func(*args, **kwargs) # Perform the package check for package in required_packages: if not is_package_available(package): pytest.skip(f"{package} not installed") return func(*args, **kwargs) return wrapper def require_package(package_name): """ Decorator that skips the test if the specified package is not installed. """ def decorator(func): @wraps(func) def wrapper(*args, **kwargs): if not is_package_available(package_name): pytest.skip(f"{package_name} not installed") return func(*args, **kwargs) re [truncated β€” 35 more characters] ``` [415 more indexed source files omitted to keep this export small. The full file list is in the Codebase structure section above.]