GraspADMM: Improving Dexterous Grasp Synthesis via ADMM Optimization

2026-03-14

AI Summary

Existing dexterous grasp synthesis methods struggle to simultaneously ensure penetration-free contacts, kinematic feasibility, and dynamic stability. This work introduces the Alternating Direction Method of Multipliers (ADMM) to this task for the first time, decoupling desired contact points from actual hand contact locations and alternately optimizing grasp dynamic metrics and hand pose. This approach maintains sampling diversity while satisfying collision-free and multi-finger force-closure constraints. By integrating dense sampling, simulation refinement, and multi-contact force optimization, the proposed method achieves an absolute improvement of nearly 15% in grasp success rate for category-agnostic synthesis and approximately 100% relative improvement for category-specific synthesis, demonstrating exceptional robustness even under low-friction conditions.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• dexterous grasp synthesis
• kinematic feasibility
• dynamic stability
• contact optimization
• grasp diversity

Innovation

• GraspADMM
• ADMM optimization
• dexterous grasp synthesis
• kinematic feasibility
• dynamic stability