Humanoid-Gym: RL Framework for Sim2Real Transfer

Project Overview

Introduction

Humanoid-Gym is a reinforcement learning (RL) framework tailored for training locomotion skills in humanoid robots. Built on Nvidia Isaac Gym, it focuses on achieving zero-shot transfer from simulation to real-world environments, ensuring that policies learned in simulation can be directly applied to physical robots without additional training. The framework also includes a sim-to-sim transfer capability, allowing users to test policies across different physical simulations to enhance their robustness and generalization. Verified by real-world tests on RobotEra’s XBot-S and XBot-L humanoid robots, Humanoid-Gym offers a practical and reliable solution for developing advanced robotic capabilities.

Key Features

Humanoid-Gym is a reinforcement learning (RL) framework designed for training locomotion skills in humanoid robots, with a focus on zero-shot transfer from simulation to real-world environments. Here are its key features:

• Humanoid Robot Training: Provides comprehensive guidelines, step-by-step configuration instructions, and execution scripts for training humanoid robots, using RobotEra’s XBot-L as a primary example.
• Sim2Sim Support: Allows transferring trained policies to different simulated environments, such as Mujoco, to ensure robustness and generalization.
• Denoising World Model Learning (DWL): An advanced framework integrating state estimation and system identification for improved sim-to-real adaptability and state estimation.
• Installation and Usage: Detailed instructions for setting up the environment, including specific requirements for Python, PyTorch, and NVIDIA drivers.
• Code Structure: Organized with clear inheritance and registration mechanisms for adding new environments and tasks.
• Troubleshooting: Guides for common issues and solutions.

The project aims to facilitate efficient and effective training of humanoid robots for real-world locomotion tasks.

Real-World Applications

The humanoid-gym project offers a versatile and comprehensive framework for training humanoid robots using reinforcement learning (RL), with a focus on zero-shot transfer from simulation to real-world environments. Here are some practical examples of how users can benefit from this repository:

• Users can follow the detailed training guidelines and step-by-step configuration instructions to train their humanoid robots for locomotion tasks. The repository provides execution scripts that simplify the training process, making it easier to set up and deploy RL policies.

Sim2Sim Transfer

• The sim2sim framework allows users to transfer trained policies from one simulated environment (e.g., Isaac Gym) to another (e.g., Mujoco), ensuring the robustness and generalization of the policies. This feature is particularly useful for verifying the performance of trained policies in different simulated scenarios before deploying them in real-world settings.

Denoising World Model Learning

• The advanced Denoising World Model Learning (DWL) framework integrates state estimation and system identification, enhancing the adaptability and effectiveness of the robot’s learning in real-world contexts. This technique is especially beneficial for optimizing the transition from simulated to real environments.

Usage Examples

• Users can run commands to initiate PPO policy training, evaluate trained policies, and perform sim2sim transformations. For example:

  bash

  python scripts/train.py --task=humanoid_ppo --run_name v1 --headless --num_envs 4096
  python scripts/play.py --task=humanoid_ppo --run_name v1
  python scripts/sim2sim.py --load_model /path/to/logs/XBot_ppo/exported/policies/policy_1.pt

Customization and Expansion

• Users can add new environments by creating new configuration files, modifying existing environments, and registering their custom environments. This flexibility allows for the adaptation of the framework to various robot models and tasks.

By leveraging these features, users can efficiently train and deploy RL policies for humanoid robots, ensuring robust and generalized performance in both simulated and real-world environments.

Conclusion

Impact and Future Potential of Humanoid-Gym:

• Zero-Shot Sim2Real Transfer: Humanoid-Gym enables seamless transfer of reinforcement learning policies from simulation to real-world environments, tested on RobotEra’s XBot-S and XBot-L robots.
• Comprehensive Training Framework: Provides detailed guidelines, step-by-step configuration, and execution scripts for training humanoid robots, ensuring robust and generalized policies.
• Sim2Sim Support: Allows policy verification in different simulated environments, such as Mujoco, to enhance trustworthiness and applicability.
• Advanced Learning Techniques: Incorporates Denoising World Model Learning for improved state estimation and system identification, enhancing real-world adaptability.
• Future Extensions: Planned features include perceptive locomotion learning and dexterous hand manipulation, expanding the capabilities of humanoid robots.

This project streamlines the development and deployment of humanoid robot locomotion skills, promising significant advancements in robotics and AI.

For further insights and to explore the project further, check out the original roboterax/humanoid-gym repository.

Attributions

Content derived from the roboterax/humanoid-gym repository on GitHub. Original materials are licensed under their respective terms.