LIPM誘導強化学習による二足歩行ロボットの安定的かつ知覚的な歩行
LIPM-Guided Reinforcement Learning for Stable and Perceptive Locomotion in Bipedal Robots
LIPMに基づく報酬設計と二重クリティック強化学習を用いて、不整地でもカメラ視点を安定させながら歩行できる二足ロボットの制御手法を提案し、実機で検証した。
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著者: Haokai Su, Haoxiang Luo, Shunpeng Yang, Kaiwen Jiang, Wei Zhang, Hua Chen
分類: cs.RO
原文アブストラクト
Achieving stable and robust perceptive locomotion for bipedal robots in unstructured outdoor environments remains a critical challenge due to complex terrain geometry and susceptibility to external disturbances. In this work, we propose a novel reward design inspired by the Linear Inverted Pendulum Model (LIPM) to enable perceptive and stable locomotion in the wild. The LIPM provides theoretical guidance for dynamic balance by regulating the center of mass (CoM) height and the torso orientation. These are key factors for terrain-aware locomotion, as they help ensure a stable viewpoint for the robot's camera. Building on this insight, we design a reward function that promotes balance and dynamic stability while encouraging accurate CoM trajectory tracking. To adaptively trade off between velocity tracking and stability, we leverage the Reward Fusion Module (RFM) approach that prioritizes stability when needed. A double-critic architecture is adopted to separately evaluate stability and locomotion objectives, improving training efficiency and robustness. We validate our approach through extensive experiments on a bipedal robot in both simulation and real-world outdoor environments. The results demonstrate superior terrain adaptability, disturbance rejection, and consistent performance across a wide range of speeds and perceptual conditions.
