iWalker: 歩行人型ロボットのための命令的視覚計画
iWalker: Imperative Visual Planning for Walking Humanoid Robot
視覚に基づく障害物回避と足場計画を同時に行う、エンドツーエンドの人間型ロボット歩行システムを提案。自己教師あり学習によりシミュレーションと実環境で有効性を実証した。
著者: Xiao Lin, Yuhao Huang, Taimeng Fu, Xiaobin Xiong, Chen Wang
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Humanoid robots, designed to operate in human-centric environments, serve as a fundamental platform for a broad range of tasks. Although humanoid robots have been extensively studied for decades, a majority of existing humanoid robots still heavily rely on complex modular frameworks, leading to inflexibility and potential compounded errors from independent sensing, planning, and acting components. In response, we propose an end-to-end humanoid sense-plan-act walking system, enabling vision-based obstacle avoidance and footstep planning for whole body balancing simultaneously. We designed two imperative learning (IL)-based bilevel optimizations for model-predictive step planning and whole body balancing, respectively, to achieve self-supervised learning for humanoid robot walking. This enables the robot to learn from arbitrary unlabeled data, improving its adaptability and generalization capabilities. We refer to our method as iWalker and demonstrate its effectiveness in both simulated and real-world environments, representing a significant advancement toward autonomous humanoid robots.
