LeRobot is a large, modular robotics and robot-learning framework maintained by Hugging Face. Although it contains substantial research code (20+ policy implementations, reward models, dataset formats, and RL infrastructure), its dominant character is that of a production-grade application/library: it exposes a rich CLI surface (lerobot-train, lerobot-eval, lerobot-record, lerobot-calibrate, lerobot-teleoperate, lerobot-annotate, lerobot-rollout), supports real hardware robots, teleoperators, cameras, and motor buses, and provides a well-structured Python package that can be embedded into external applications. The repo is best described as hybrid: a research codebase packaged as an installable robotics application framework.
GELLO is a general, low-cost teleoperation framework for robot manipulators. It uses a Dynamixel-based leader device to control various follower robots (Franka FR3, YAM, UR, xArm, Panda) in real-time via ZMQ-based distributed architecture. The system supports both simulation (MuJoCo) and real hardware, includes gravity compensation (FACTR) for YAM robots, and provides data collection pipelines for imitation learning. It also offers ROS2 integration for Franka FR3 with custom joint impedance controllers and gripper managers. The codebase is configuration-driven via YAML files and demonstrates strong engineering practices with pre-commit hooks, CI workflows, and reusable abstractions for robots, agents, and cameras.
This repository is the complete open-source hardware and software stack for the Roboto Origin bipedal robot, formerly known as Atom01. It is a hybrid monorepo covering mechanical design (CAD files, 3D printing, BOMs), electronics (PCB designs, firmware for a USB2CAN board), low-level ROS2 control (motor drivers, IMU, camera, inference), simulation and reinforcement learning training (roboparty_train with Isaac Sim/MuJoCo and PPO/AMP algorithms), navigation (LiDAR localization, UWB, path planning), and XR teleoperation. The repository is organized into multiple modules, each with its own build system (CMake, Zephyr, Python). The dominant character is an application (a robot system), but it includes a substantial data_research component in the training module where RL policies are trained using motion capture data. The codebase is well-structured, actively maintained, and provides a complete recipe for building and deploying a humanoid robot from scratch.
This repository provides a functional system for motion retargeting from human video to robot models. It detects arm and hand poses from monocular video using MediaPipe, fuses the detections, applies multi-stage coordinate transformations (world → first-person → URDF standard), and retargets the motion to robot URDF models using numerical optimization. The system includes visualization tools for debugging coordinate transformations and verifying retargeting results. The architecture is modular with separate components for detection (`src/whobody_dect/`), retargeting (`src/dex_retargeting/`), and example workflows (`example/`). Key entry points include `example/vector_retargeting/retarget_from_wholebody_video.py` for the main retargeting pipeline and `src/whobody_dect/simple_visualize.py` for coordinate system validation.