CoorGrasp: 不確実性下での適応的器用把持のための協調接触制御
CoorGrasp: Coordinated Contact Control for Adaptive Dexterous Grasping Under Uncertainty
形状や位置の不確実性下で計画された器用把持を実行する際、開ループ実行では接触が不協調になり失敗しやすい問題に対し、触覚駆動のモデル予測制御を提案し、接近・把持の両フェーズで多指の接触を協調させて成功率を向上させた。
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？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Mingrui Yu, Yongpeng Jiang, Yongyi Jia, Yi Ren, Xiang Li
分類: cs.RO
原文アブストラクト
While recent research has focused heavily on dexterous grasp pose generation, less attention has been devoted to the execution of planned grasps. Under shape and position uncertainty, open-loop execution often yields uncoordinated contacts, causing undesired in-hand object motion and even grasp failures. To address this, this paper proposes a tactile-driven model predictive controller for adaptive and delicate execution of diverse dexterous grasps. Our approach emphasizes multi-contact coordination across both approaching and grasping phases, with three key novelties: (i) coordination-aware phase separation, (ii) arm-hand coordination to compensate for position errors, and (iii) adaptive force coordination to increase contact forces in a balanced manner. An analytical model is employed to relate contact forces to robot joint motions for predictive control. Our formulation imposes no restrictions on grasp types or contact configurations and integrates seamlessly with state-of-the-art grasp pose generation methods. We validate the approach through large-scale simulations involving 15k grasps across 478 objects on three robotic hands, and real-world experiments on 8 objects. Results demonstrate that our method achieves higher grasp success rates and reduced undesired object movements.
