衝撃に備える：低次元モデルによるロバストなヒューマノイドの押し戻し回復と歩行
Bracing for Impact: Robust Humanoid Push Recovery and Locomotion with Reduced Order Models
壁などの環境を利用して腕で踏ん張ることで、歩行中のヒューマノイドが押し戻しから回復する統合フレームワークを提案し、シミュレーションで有効性を示した。
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: Lizhi Yang, Blake Werner, Adrian B. Ghansah, Aaron D. Ames
分類: cs.RO
原文アブストラクト
Push recovery during locomotion will facilitate the deployment of humanoid robots in human-centered environments. In this paper, we present a unified framework for walking control and push recovery for humanoid robots, leveraging the arms for push recovery while dynamically walking. The key innovation is to use the environment, such as walls, to facilitate push recovery by combining Single Rigid Body model predictive control (SRB-MPC) with Hybrid Linear Inverted Pendulum (HLIP) dynamics to enable robust locomotion, push detection, and recovery by utilizing the robot's arms to brace against such walls and dynamically adjusting the desired contact forces and stepping patterns. Extensive simulation results on a humanoid robot demonstrate improved perturbation rejection and tracking performance compared to HLIP alone, with the robot able to recover from pushes up to 100N for 0.2s while walking at commanded speeds up to 0.5m/s. Robustness is further validated in scenarios with angled walls and multi-directional pushes.
