Stable-worldmodel is a research framework for goal-conditioned world models in reinforcement learning, providing a full pipeline from data collection and storage to model training and planning-based evaluation. The codebase is primarily a research platform with strong software engineering practices—pluggable data formats (Lance, HDF5, video, folder), a command-line interface (swm), and a Hydra-based configuration system for reproducible experiments. It includes implementations of multiple world model architectures (LeWM, PLDM, PreJEPA/DINO-WM, TD-MPC2, GCRL), planning solvers (CEM, MPPI, iCEM, gradient-based, Lagrangian), and environment wrappers with domain randomization across 30+ tasks (DeepMind Control, Gymnasium, PushT, OGBench, Craftax, robotics). The repository demonstrates high craftsmanship in its data engineering: the data format registry extends cleanly, the Lance format supports cloud-native streaming from Hugging Face Buckets, and utilities like GoalDataset, HistoryBuffer, and ReplayBuffer solve recurring RL data management problems. The project is actively maintained with extensive test coverage, comprehensive documentation, and a scalable training pipeline using stable-pretraining and Lightning. While the primary goal is research (testing hypotheses about world model generalization under variation), the codebase includes application-level production quality—CI/CD, CLI, checkpoint management, and robust error handling—making it a rare hybrid that serves both researchers and engineers.
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.