GraspADMM: Improving Dexterous Grasp Synthesis via ADMM Optimization

TL;DR

GraspADMM introduces an ADMM-based optimization framework that enhances dexterous grasp synthesis by balancing diversity, kinematic feasibility, and dynamic stability, significantly outperforming existing methods in success rates.

Contribution

It presents a novel ADMM-based approach that decouples contact point optimization from hand pose adjustments, improving grasp quality and physical plausibility over prior fixed-contact and gradient-based methods.

Findings

Achieves nearly 15% higher grasp success rate in type-unaware synthesis.

Doubles success rate in type-aware grasp synthesis.

Maintains robust grasping under low-friction conditions.

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.