BAT: オンライン方策切替による俊敏性と安定性の両立 - 長期的全身ヒューマノイド制御
BAT: Balancing Agility and Stability via Online Policy Switching for Long-Horizon Whole-Body Humanoid Control
長期的な全身動作タスクで俊敏性と安定性を両立するため、2つの補完的な強化学習方策をオンラインで切り替えるフレームワークBATを提案し、シミュレーションと実機で有効性を示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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6. 次に読むべき論文は？
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著者: Donghoon Baek, Sang-Hun Kim, Sehoon Ha
分類: cs.RO
原文アブストラクト
Despite recent advances in control, reinforcement learning, and imitation learning, developing a unified framework that can achieve agile, precise, and robust whole-body behaviors, particularly in long-horizon tasks, remains challenging. Existing approaches typically follow two paradigms: coupled whole-body policies for global coordination and decoupled policies for modular precision. However, without a systematic method to integrate both, this trade-off between agility, robustness, and precision remains unresolved. In this work, we propose BAT, an online policy-switching framework that dynamically selects between two complementary whole-body RL controllers to balance agility and stability across different motion contexts. Our framework consists of two complementary modules: a switching policy learned via hierarchical RL with an expert guidance from sliding-horizon policy pre-evaluation, and an option-aware VQ-VAE that predicts option preference from discrete motion token sequences for improved generalization. The final decision is obtained via confidence-weighted fusion of two modules. Extensive simulations and real-world experiments on the Unitree G1 humanoid robot demonstrate that BAT enables versatile long-horizon loco-manipulation and outperforms prior methods across diverse tasks.
