生LiDAR点群から直接制御する人型ロボットの安全で快適な歩行ポリシー
End-to-End Humanoid Robot Safe and Comfortable Locomotion Policy
生のLiDAR点群を直接モータ指令に変換するエンドツーエンド歩行ポリシーを提案し、制約付きMDPと制御バリア関数を組み合わせて安全性を保証、快適性報酬で人間に配慮した動きを実現、実機へ転移した。
詳しい要約
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著者: Zifan Wang, Xun Yang, Jianzhuang Zhao, Jiaming Zhou, Teli Ma, Ziyao Gao, Arash Ajoudani, Junwei Liang
分類: cs.RO
原文アブストラクト
The deployment of humanoid robots in unstructured, human-centric environments requires navigation capabilities that extend beyond simple locomotion to include robust perception, provable safety, and socially aware behavior. Current reinforcement learning approaches are often limited by blind controllers that lack environmental awareness or by vision-based systems that fail to perceive complex 3D obstacles. In this work, we present an end-to-end locomotion policy that directly maps raw, spatio-temporal LiDAR point clouds to motor commands, enabling robust navigation in cluttered dynamic scenes. We formulate the control problem as a Constrained Markov Decision Process (CMDP) to formally separate safety from task objectives. Our key contribution is a novel methodology that translates the principles of Control Barrier Functions (CBFs) into costs within the CMDP, allowing a model-free Penalized Proximal Policy Optimization (P3O) to enforce safety constraints during training. Furthermore, we introduce a set of comfort-oriented rewards, grounded in human-robot interaction research, to promote motions that are smooth, predictable, and less intrusive. We demonstrate the efficacy of our framework through a successful sim-to-real transfer to a physical humanoid robot, which exhibits agile and safe navigation around both static and dynamic 3D obstacles.
