実行可能動作事前分布によるヒューマノイドロボットの立位上半身動作模倣
EMP: Executable Motion Prior for Humanoid Robot Standing Upper-body Motion Imitation
ヒューマノイドが立ったまま人間の上半身動作を模倣する強化学習フレームワークを提案し、ロボットの実行可能範囲を考慮するEMPモジュールで安定性を確保した。
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著者: Haocheng Xu, Haodong Zhang, Zhenghan Chen, Rong Xiong
分類: cs.RO
原文アブストラクト
To support humanoid robots in performing manipulation tasks, it is essential to study stable standing while accommodating upper-body motions. However, the limited controllable range of humanoid robots in a standing position affects the stability of the entire body. Thus we introduce a reinforcement learning based framework for humanoid robots to imitate human upper-body motions while maintaining overall stability. Our approach begins with designing a retargeting network that generates a large-scale upper-body motion dataset for training the reinforcement learning (RL) policy, which enables the humanoid robot to track upper-body motion targets, employing domain randomization for enhanced robustness. To avoid exceeding the robot's execution capability and ensure safety and stability, we propose an Executable Motion Prior (EMP) module, which adjusts the input target movements based on the robot's current state. This adjustment improves standing stability while minimizing changes to motion amplitude. We evaluate our framework through simulation and real-world tests, demonstrating its practical applicability.
