This repository implements a complete reinforcement learning pipeline for whole-body motion tracking on humanoid robots, specifically Unitree G1 and XBot L7. The system uses a teacher-student architecture with Proximal Policy Optimization (PPO) to train policies that track reference motions from retargeted motion capture datasets. It features three training phases: a base training phase, an adaptation phase with domain randomization, and a finetuning phase. The environment is built on MJLab (a MuJoCo wrapper) and includes extensive domain randomization (e.g., motor parameters, body mass, center of mass, joint offsets, external forces, and a window load capacity curriculum for payload-aware training). The codebase is highly modular, with registered MDP components (observations, rewards, terminations, randomizations) and a flexible configuration system using Hydra. It also includes advanced features such as symmetry augmentation, differentiable forward kinematics, and a custom motion dataset pipeline with memory-mapped storage. While primarily a reinforcement learning training framework, it has strong data engineering aspects for processing motion datasets and generating training data for the student encoder. The repository is well-structured for research and sim-to-real transfer, but it is tightly coupled to specific robot assets and dependencies (torch, torchrl, mjlab) with pinned versions that may require adaptation for other environments.
This repository implements a Whole-Body Control (WBS) deployment system for Unitree G1 humanoid robots. It uses a finite state machine (FSM) architecture with multiple control states including Passive, FixedStand, Loco (locomotion), AMP, MJAMP, and WBC (Whole-Body Control). The system processes real-time sensor data from the robot's IMU and joint encoders, runs inference on ONNX models to generate joint position commands, and accepts gamepad input to switch between control states. The application is designed for real-time operation with proper scheduling and safety considerations.
This repository is a curated 'awesome' list of biomechanical resources. It contains no executable code, notebooks, or data pipelines. The entire content is a single README.md file with hundreds of categorized links to learning materials, datasets, software tools, books, societies, and other resources relevant to biomechanics research and education. It functions as a community-maintained directory rather than a software project or research pipeline. While the list is well-organized and frequently updated, there are no computational components to analyze or reuse.