DEAL-Grasp: 幾何認識型巧みな把持生成のための分離アライメント表現
DEAL-Grasp: Decoupled Alignment Representation for Geometry-Aware Dexterous Grasp Generation
把持生成を「アライメント空間」での生成問題として再定式化し、剛体運動と関節運動を分離して流れマッチングで安定かつ物理的に妥当な巧みな把持を生成する手法を提案。
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著者: Fuqiang Zhao, Qian Liu
分類: cs.RO, cs.GR
原文アブストラクト
Synthesizing realistic articulated hand-object interactions is a fundamental problem in virtual reality, embodied intelligence, and digital human applications. Existing methods for dexterous grasp synthesis typically regress or denoise poses in a joint space that couples global rigid motion with local articulation, which often yields unstable samples and physically implausible contacts. We introduce DEAL-Grasp, built upon the Decoupled Alignment (DEAL) representation, which reformulates grasp synthesis as alignment-space generation: the interaction state comprises task-space geometric anchors and articulation parameters, from which the rigid transform is recovered via closed-form Procrustes alignment while preserving local articulation. On this mixed state, we model grasp generation using heterogeneous-state flow matching with component-wise vector fields, incorporating time-adaptive physical regularization during training. At inference, grasps are synthesized solely by integrating the learned vector field, without test-time optimization or auxiliary physical guidance. Across MultiDex and zero-shot RealDex benchmarks, DEAL-Grasp attains high force-perturbation success rates alongside minimal penetration and high diversity of generated grasps, while substantially reducing native inference latency compared to optimization-heavy baselines. The project page is available at https://wmtlab.github.io/DEAL-Grasp/.