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REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning
arXiv – CS AI|Zhaoyuan Gu, Yipu Chen, Zimeng Chai, Alfred Cueva, Thong Nguyen, Yifan Wu, Huishu Xue, Minji Kim, Isaac Legene, Fukang Liu, Matthew Kim, Ayan Barula, Yongxin Chen, Ye Zhao|
🤖AI Summary
Researchers developed REFINE-DP, a hierarchical framework that combines diffusion policies with reinforcement learning to enable humanoid robots to perform complex loco-manipulation tasks. The system achieves over 90% success rate in simulation and demonstrates smooth autonomous execution in real-world environments for tasks like door traversal and object transport.
Key Takeaways
- →REFINE-DP addresses the challenge of deploying diffusion policies on humanoid robots by jointly optimizing high-level planning and low-level control.
- →The framework uses PPO-based diffusion policy gradient to fine-tune motion planners while simultaneously updating controllers to reduce distributional mismatch.
- →The system achieves over 90% success rate in simulation, even for out-of-distribution cases not seen in training data.
- →Real-world validation shows smooth autonomous task execution in dynamic environments for complex loco-manipulation tasks.
- →The approach substantially outperforms pre-trained diffusion policy baselines, demonstrating the importance of RL fine-tuning for humanoid robotics.
#humanoid-robotics#diffusion-policy#reinforcement-learning#robotics#ai-research#locomotion#manipulation#arxiv
Read Original →via arXiv – CS AI
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