DAVIS: ヒューマノイドサッカースキルのための深度のみのエンドツーエンド能動視覚フレームワーク
DAVIS: A Depth-Only End-to-End Active-Vision Framework for Humanoid Soccer Skills
頭部搭載の深度画像と固有感覚履歴のみから、ヒューマノイドロボットがサッカーのシュートやドリブルを直接25自由度の関節PD目標として学習するエンドツーエンドフレームワークを提案。
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？
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著者: Jiakang Jin, Yixiao Huo, Pengyuan Wang, Yinan Han, Tingxuan Zhang, Zhuobing Zhao, Xuanxin Zhou, Zhangchen Ye, Enxuan Ruan, Yifei Bao, Jiankun Yang, Chenghao Sun, Wenhao Cui, Xiaoyu Tian, Yiming Li
分類: cs.RO
原文アブストラクト
Humanoid soccer contact skills require more than producing high-impact foot-ball contacts: the robot must close the loop over perception, approach, alignment, impact, and recovery while its own motion induces substantial viewpoint changes, frequent loss of the ball from view, and uncertain contact outcomes. In this work, we ask a compact yet stricter question: can a humanoid learn soccer contact skills using only a head-mounted depth image, proprioceptive history, and an optional low-dimensional task command, and directly output 25-DoF joint PD targets without extra runtime perception or planning modules? To this end, we propose DAVIS, a depth-only end-to-end framework for humanoid soccer skills that learns visibility-aware auxiliary geometry during training, and combines GT-to-prediction annealing, task curricula, and AMP-style motion priors to smoothly bridge privileged supervision and real deployment. Built on this framework, we instantiate representative soccer contact skills, including goal-directed shooting and directional dribbling, through task-specific definitions of objects, commands, rewards, and curricula, and validate them through simulation, Noetix E1 real-robot experiments, and ablations.