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GraspADMM: Improving Dexterous Grasp Synthesis via ADMM Optimization

Liangwang Ruan, Jiayi Chen, He Wang, Baoquan Chen

Abstract

Synthesizing high-quality dexterous grasps is a fundamental challenge in robot manipulation, requiring adherence to diversity, kinematic feasibility (valid hand-object contact without penetration), and dynamic stability (secure multi-contact forces). The recent framework Dexonomy successfully ensures broad grasp diversity through dense sampling and improves kinematic feasibility via a simulator-based refinement method that excels at resolving exact collisions. However, its reliance on fixed contact points restricts the hand's reachability and prevents the optimization of grasp metrics for dynamic stability. Conversely, purely gradient-based optimizers can maximize dynamic stability but rely on simplified contact approximations that inevitably cause physical penetrations. To bridge this gap, we propose GraspADMM, a novel grasp synthesis framework that preserves sampling-based diversity while improving kinematic feasibility and dynamic stability. By formulating the refinement stage using the Alternating Direction Method of Multipliers (ADMM), we decouple the target contact points on the object from the actual contact locations on the hand. This decomposition allows the pipeline to alternate between updating the target object points to directly maximize dynamic grasp metrics, and adjusting the hand pose to physically reach these targets while strictly respecting collision boundaries. Extensive experiments demonstrate that GraspADMM significantly outperforms state-of-the-art baselines, achieving a nearly 15\% absolute improvement in grasp success rate for type-unaware synthesis and roughly a 100\% relative improvement in type-aware synthesis. Furthermore, our approach maintains robust, physically plausible grasp generation even under extreme low-friction conditions.

GraspADMM: Improving Dexterous Grasp Synthesis via ADMM Optimization

Abstract

Synthesizing high-quality dexterous grasps is a fundamental challenge in robot manipulation, requiring adherence to diversity, kinematic feasibility (valid hand-object contact without penetration), and dynamic stability (secure multi-contact forces). The recent framework Dexonomy successfully ensures broad grasp diversity through dense sampling and improves kinematic feasibility via a simulator-based refinement method that excels at resolving exact collisions. However, its reliance on fixed contact points restricts the hand's reachability and prevents the optimization of grasp metrics for dynamic stability. Conversely, purely gradient-based optimizers can maximize dynamic stability but rely on simplified contact approximations that inevitably cause physical penetrations. To bridge this gap, we propose GraspADMM, a novel grasp synthesis framework that preserves sampling-based diversity while improving kinematic feasibility and dynamic stability. By formulating the refinement stage using the Alternating Direction Method of Multipliers (ADMM), we decouple the target contact points on the object from the actual contact locations on the hand. This decomposition allows the pipeline to alternate between updating the target object points to directly maximize dynamic grasp metrics, and adjusting the hand pose to physically reach these targets while strictly respecting collision boundaries. Extensive experiments demonstrate that GraspADMM significantly outperforms state-of-the-art baselines, achieving a nearly 15\% absolute improvement in grasp success rate for type-unaware synthesis and roughly a 100\% relative improvement in type-aware synthesis. Furthermore, our approach maintains robust, physically plausible grasp generation even under extreme low-friction conditions.
Paper Structure (24 sections, 10 equations, 6 figures, 4 tables, 1 algorithm)

This paper contains 24 sections, 10 equations, 6 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Analytical Grasp Synthesis
  4. Alternating Direction Method of Multipliers (ADMM)
  5. Method
  6. Pipeline Overview
  7. Grasp Quality Metric
  8. ADMM Optimization
  9. 1. Fix $\mathbf{q},\mathbf{p}^h,\lambda$, update $\mathbf{p}^o$
  10. 2. Fix $\mathbf{p}^o,\lambda$, update $\mathbf{q},\mathbf{p}^h$
  11. 3. Fix $\mathbf{q},\mathbf{p}^h,\mathbf{p}^o$, update $\lambda$
  12. Experiments
  13. Evaluation Metric
  14. Type-unaware Grasp Synthesis
  15. Type-Aware Grasp Synthesis
  16. ...and 9 more sections

Figures (6)

  • Figure 1: Overview. From the same initialization, our GraspADMM framework generates robust grasps by optimizing contact points on the hand (red) and the object (yellow), while Dexonomy chen2025dexonomy simply fixes these points.
  • Figure 2: ADMM Optimization Pipeline. (a) Update target object contact points $\mathbf{p}^o$ via gradient descent to maximize dynamic stability. (b) Update hand pose $\mathbf{q}$ and points $\mathbf{p}^h$ via forward-simulated transposed Jacobian control to satisfy kinematic feasibility. (c) Update the dual variable $\lambda$.
  • Figure 3: Visualization of Optimized Grasps. Comparison using the Allegro hand (blue) and Shadow hand (black). Given identical initializations, our method synthesizes more physically stable grasps than Dexonomy.
  • Figure 4: Performance on the Hard Benchmark. Grasp success rate and object success rate across varying tangential friction coefficients. Our method consistently outperforms Dexonomy.
  • Figure 5: Robustness under Extreme Low Friction ($\mu=0.1$). While Dexonomy struggles to find more than a single stable grasp out of 100 attempts for the shown objects, GraspADMM reliably generates a diverse set of stable grasps.
  • ...and 1 more figures