2026-08-17 · RA-L · 1 citations

Arm-Aware Guided Dexterous Grasp Generation with Arm-Agnostic Grasp Models

Yongyi Jia, Yongpeng Jiang, Kangchen Lv, Yi Ren, Mingrui Yu, Xiang Li

Published at RA-L (the arXiv record still lists it as a preprint). 1 citations, as of the last refresh.

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/.

Ten-minute slide kit

Six slides is the whole talk: what was broken, what people tried, what these authors did, what the numbers say, where it falls over, and the sentence people should remember.

SLIDE 1

The problem

D EXTEROUS grasp generation, which generates grasp poses for dexterous hands based on object and environment information, provides a target grasp configuration for grasp execution [\[1\]](#page-7-0), [\[2\]](#page-7-1), and serves as a prerequisite for the subsequent robotic manipulation [\[3\]](#page-7-2). Existing grasp generation methods predominantly employ a *hand-centric* scheme, which focuses primarily on learning the distribution of a free-floating hand's grasp poses, overlooking the robotic arm and…

SLIDE 2

What came before

Not recoverable from the parsed text. Read this section in the paper yourself.

SLIDE 3

The method

Yongyi Jia† , Yongpeng Jiang† , Kangchen Lv, Yi Ren, Mingrui Yu∗ , and Xiang Li∗ *Abstract*—Dexterous grasp generation that considers armrelated constraints is crucial in real-world scenarios involving armenvironment 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…

SLIDE 4

What they measured

Additional evaluation results can be found in the Appendix (available on our Project Website\). We use the Shadow Hand in simulations and the LEAP Hand for real-world evaluations, considering two common robotic arms, UR5 and Franka. We assume the environment can be represented as a combination of closed geometries, enabling a well-defined SDF. We adopt the sphere robot collision models from cuRobo [\[29\]](#page-7-28) for collision

SLIDE 5

Where it breaks

We present several failure cases in Fig. 15, highlighting potential limitations of the proposed method and possible directions for improvement. 1) In some cases, the guidance compromises the quality of a small subset of generated grasps, resulting in unstable configurations that only grasp the edge of the object (Fig. 15 (a-b)). This accounts for 7 out of 20 failed grasps. This issue often arises when the arm remains in collision until the end of the denoising process, causing conflicts between constraint…

SLIDE 6

One-line takeaway

This letter forms arm-aware constrained grasp generation as a joint optimization of hand pose and arm configuration, and derive closed-form gradients for arm-related constraints, and proves that gradient-based optimization is equivalent to guided diffusion sampling, steering near-feasible samples toward the feasible region.

Assembled from the paper's own PDF, parsed with its layout intact so tables and equations survive, 72,804 characters of it, then split on the paper's own section headings. Extractive, not generated: every sentence here is lifted from the paper. Check it before you present it.