Arm-Aware Guided Dexterous Grasp Generation with Arm-Agnostic Grasp Models
2026-08-17 • Robotics
Robotics
AI summaryⓘ
The authors address the challenge of generating robot grasps that consider both the hand and arm, which is important for avoiding collisions and working in tight spaces. They point out that current methods either ignore the arm or require costly retraining for different robots and environments. Their new approach uses existing hand-focused grasp models and adds arm and environment constraints only when making a grasp, improving efficiency and adaptability. They tested their method on many objects and scenarios, showing it creates feasible grasps more often in difficult situations.
dexterous grasprobot arm constraintscollision avoidancediffusion modelgrasp generationjoint optimizationrejection samplingguided diffusion samplingrobot workspace
Authors
Yongyi Jia, Yongpeng Jiang, Kangchen Lv, Yi Ren, Mingrui Yu, Xiang Li
Abstract
Dexterous grasp generation that considers arm-related constraints is crucial in real-world scenarios involving arm environment collision avoidance, workspace boundary grasps, and consecutive grasping. Existing hand-centric grasp models, which primarily focus on the floating hand's pose, are insufficient for such cases. Conventional arm-aware methods either rely on rejection sampling to discard infeasible samples or require retraining on arm-specific data, leading to low sample efficiency under adverse conditions or limited generalization across different robots and environments. To overcome these limitations, this letter presents an arm-aware dexterous grasp generation framework that leverages pretrained arm-agnostic grasp models while integrating arm and environmental information only at inference time. Specifically, we formulate arm-aware constrained grasp generation as a joint optimization of hand pose and arm configuration, and derive closed-form gradients for arm-related constraints. Assuming the hand pose distribution is represented by a diffusion model, we prove that gradient-based optimization is equivalent to guided diffusion sampling, steering near-feasible samples toward the feasible region. Through comprehensive evaluation involving 10k objects across 6 scenarios, we demonstrate that the proposed framework generates feasible grasps in highly constrained settings with significantly higher probability, highlighting its advantages in real-world applications. Supplementary materials and appendix are available at https://arm-aware-dexgrasp.github.io/.