AdaDexGrasp: 3D視覚触覚表現融合による適応的器用把持
AdaDexGrasp: Adaptive Dexterous Grasping via 3D Visuo-Tactile Representation Fusion
視覚と触覚を統合した表現を用いて、把持計画と接触後の適応的修正を行う器用なロボットハンドの把持フレームワークを提案し、シミュレーションと実環境で成功率と汎化性を向上させた。
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著者: Xirui Liang, Jiaqi Liang, Jingkai Xu, Yuran Wang, Ruochong Li, Yuanpei Chen, Masayoshi Tomizuka, Wei Zhan, Ruihai Wu
分類: cs.RO, eess.IV
原文アブストラクト
Humans achieve stable and adaptive grasps by seamlessly integrating visual perception and tactile feedback, a capability that remains challenging to replicate in robotic systems. Existing robotic grasping approaches predominantly rely on visual inputs and lack mechanisms for tactile-guided adaptation after contact, limiting robustness and generalization. To address this challenge, we propose a unified visuo-tactile-fusion grasping framework that integrates grasp generation, feasibility prediction, and adaptive refinement. At its core, our method introduces an efficient visuo-tactile representation that tightly fuses object geometry with tactile feedback by associating tactile signals with finger identities. This unified representation supports contact-aware grasp pose generation during planning and tactile-guided refinement after contact, enabling the system to reason about fine-grained finger-object interactions and adjust grasps dynamically. Comprehensive experiments in both simulation and real-world environments demonstrate that our approach significantly enhances grasp success rates and generalization across diverse objects.