SynManDex: 合成人間プレグラスプからの人間らしい器用な把持の合成
SynManDex: Synthesizing Human-like Dexterous Grasps from Synthetic Human Pre-Grasps
人間のプレグラスプを生成し、それをロボットハンドにリターゲットして力閉ループ接触を最適化することで、人間らしく安定した器用な把持を合成するパイプラインを提案。
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？
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: Yanming Shao, Zanxin Chen, Wenwei Lin, Mingjie Zhou, Tianxing Chen, Xiaokang Yang, Yichen Chi, Yao Mu
分類: cs.RO
原文アブストラクト
Human hand-object interactions encode functional intent, but direct transfer to robotic hands often fails under morphology, contact, and reachability constraints. We present SynManDex, a synthetic pipeline that uses generated human pre-grasps as affordance-aware proposals and resolves the final contacts with robot-native optimization. SynManDex samples object-conditioned digital human pre-grasps, retargets them to dexterous robotic hand poses, optimizes force-closure contacts on the target embodiment, and admits trajectories that pass checks from each step. The resulting keyframes support both grasp-and-lift demonstrations and various prehensile manipulation tasks such as tea pouring, photo taking, and flute playing, designed via VLM agents. As a result, SynManDex combines high grasp quality (86.4\% grasp stability) with 4.67/5 human-likeness (93.4\%). It achieves 80.7\% successes in simulation and 25/30 (83.3\%) real-robot successes when applied to a 36-DOF bimanual dexterous robotic platform.
