OpenManus is a framework for training reinforcement learning agents to perform dexterous in-hand manipulation using physics simulation, motion capture data, and real-world robotic hardware.
OpenManus is an open-source framework for reinforcement learning on dexterous robotic hands, designed to facilitate research in manipulation, control, and policy learning. Built around the Manus robot hand platform, it provides a standardized environment for training and evaluating RL algorithms on complex, high-DOF manipulation tasks. The repository includes simulation environments, task definitions, and training pipelines that support reproducible experiments and rapid prototyping of new methods.
Key capabilities include configurable task setups (such as object grasping, in-hand manipulation, and trajectory tracking), integration with common RL libraries, and tools for logging, evaluation, and benchmarking. OpenManus exposes low-level control interfaces as well as higher-level abstractions, allowing researchers to experiment with different action spaces, observation modalities, and reward structures. It supports curriculum learning and domain randomization to improve policy robustness and transferability.
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