BerriAI / litellm
github.com·3d ago
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GitHub Trending·Aug 30·5 min read
pollen-robotics / microduck_rl
TL;DR
Microduck is a bipedal robot that utilizes reinforcement learning (RL) for training in simulated environments, achieving effective real-world deployment. Key outcomes include successful policy training using Proximal Policy Optimization (PPO) and a comprehensive sim2real approach.
✦ Why It Matters
Explore the microduck_rl repository to implement advanced RL techniques in your robotics projects today.
Key Takeaways
- Utilize the microduck_rl repository for RL training environments.
- Train walking policies using provided scripts and a CUDA GPU.
- Export trained policies to ONNX for real-world deployment.
- Implement domain randomization to improve sim2real performance.
How It Works
Microduck's training leverages Proximal Policy Optimization (PPO) within a MuJoCo Warp environment, allowing for high-frequency policy updates. The sim2real approach includes modeling actuator physics and simulating backlash, ensuring that the robot's performance in simulation closely matches real-world behavior.
The policies are designed to be interchangeable, enabling the robot to adapt to various tasks seamlessly.