マルチ行動蒸留と強化学習ファインチューニングによる適応型ヒューマノイド制御
Towards Adaptive Humanoid Control via Multi-Behavior Distillation and Reinforced Fine-Tuning
複数の運動スキルを蒸留して基本制御器を作り、強化学習で微調整することで、様々な地形で適応的に動けるヒューマノイド制御を実現した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Yingnan Zhao, Xinmiao Wang, Dewei Wang, Xinzhe Liu, Dan Lu, Qilong Han, Peng Liu, Chenjia Bai
分類: cs.RO
原文アブストラクト
Humanoid robots are promising to learn a diverse set of human-like locomotion behaviors, including standing up, walking, running, and jumping. However, existing methods predominantly require training independent policies for each skill, yielding behavior-specific controllers that exhibit limited generalization and brittle performance when deployed on irregular terrains and in diverse situations. To address this challenge, we propose Adaptive Humanoid Control (AHC) that adopts a two-stage framework to learn an adaptive humanoid locomotion controller across different skills and terrains. Specifically, we first train several primary locomotion policies and perform a multi-behavior distillation process to obtain a basic multi-behavior controller, facilitating adaptive behavior switching based on the environment. Then, we perform reinforced fine-tuning by collecting online feedback in performing adaptive behaviors on more diverse terrains, enhancing terrain adaptability for the controller. We conduct experiments in both simulation and real-world experiments in Unitree G1 robots. The results show that our method exhibits strong adaptability across various situations and terrains. Project website: https://ahc-humanoid.github.io.
