Anchor 1：全身マニピュレーションのための精密で安定したヒューマノイド制御
Anchor 1: Precise and Stable Humanoid Control for Whole-Body Manipulation
校正済みの疎な動作参照と明示的な誤差フィードバックを用い、オンラインの関節空間リターゲティングなしで精密かつ安定した全身マニピュレーションを実現するヒューマノイド制御器と評価ベンチマークを提案した。
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？
3. 技術・手法の肝は？
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5. 議論はある？
6. 次に読むべき論文は？
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著者: Archon Robotics, :, Caojun Wang, Checheng Yu, Gangcheng Jiang, Guangjun Zeng, Haoran Jiang, Hongyang Li, Huibin Yan, Huijie Wang, Jialong Zeng, Jin Yang, Kai Cheng, Li Chen, Liang Chen, Lirui Zhao, Modi Shi, Penglin Fu, Shijia Peng, Tianyu Li, Wencong Zhang, Xuechong Lv, Yixuan Pan, Yong Zhang, Yu Qiao, Yu Zhang, Ziyan Li
分類: cs.RO
原文アブストラクト
Humanoid controllers have acquired increasingly diverse motor skills, yet translating these capabilities into precise and stable whole-body manipulation remains challenging. Manipulation tasks are sensitive to execution deviations -- even small errors or unintended steps can cause failure. Introducing Anchor 1, a whole-body controller for precise and stable whole-body manipulation, together with a benchmark spanning diverse coordination demands. Anchor 1 adopts calibrated sparse motion references to preserve task-relevant human motion structure without online joint-space retargeting, leaving kinematic freedom for precise control. To guide this freedom towards stable, physically plausible whole-body coordination, we shape the training distribution through offline curation of robot motions paired with these calibrated sparse references. We further introduce explicit error feedback decoupled from motion encoding, improving control precision while supporting flexible command interfaces. To achieve a systematic evaluation of these capabilities, a benchmark is proposed to assess precision, sustained execution stability, and reachability across diverse whole-body coordination demands. Evaluations in simulation and on hardware show stronger precision and stability across all evaluation tracks. Under the most strict real-world global scoring setting, Anchor 1 achieves a 30\% to 80\% decrease in tracking error with fewer look-ahead inputs, and without retargeting compared to all existing controllers. Downstream experiments demonstrate more efficient teleoperated data collection and higher task success using the same deployment pipeline as well. We believe Anchor 1 supports precise and stable whole-body control as a practical basis for humanoid task execution.
