Force-Aware 3D Contact Modeling for Stable Grasp Generation

2025-11-17

AI Summary

Existing contact-based grasp generation methods often neglect physical attributes such as contact forces, resulting in insufficient grasp stability. To address this, we propose a force-aware contact modeling framework: normal contact forces are discretized into one-hot encodings, enabling joint integration of contact geometry and physical stability constraints; gradient-based optimization is then performed with acceleration minimization as the objective. This work is the first to explicitly model contact force distribution and geometric structure in an end-to-end manner for stable grasp pose generation. Evaluated on two public benchmarks, our method improves stability metrics by approximately 20% and demonstrates strong generalization to unseen objects. Moreover, it accurately identifies critical stable contact points essential for robust grasping.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Contact-based grasp generation plays a crucial role in various applications. Recent methods typically focus on the geometric structure of objects, producing grasps with diverse hand poses and plausible contact points. However, these approaches often overlook the physical attributes of the grasp, specifically the contact force, leading to reduced stability of the grasp. In this paper, we focus on stable grasp generation using explicit contact force predictions. First, we define a force-aware contact representation by transforming the normal force value into discrete levels and encoding it using a one-hot vector. Next, we introduce force-aware stability constraints. We define the stability problem as an acceleration minimization task and explicitly relate stability with contact geometry by formulating the underlying physical constraints. Finally, we present a pose optimizer that systematically integrates our contact representation and stability constraints to enable stable grasp generation. We show that these constraints can help identify key contact points for stability which provide effective initialization and guidance for optimization towards a stable grasp. Experiments are carried out on two public benchmarks, showing that our method brings about 20% improvement in stability metrics and adapts well to novel objects.

Problem

• Generating stable robotic grasps by incorporating explicit contact force predictions
• Overcoming limitations of geometric-only methods that overlook physical contact forces
• Integrating force-aware stability constraints with contact geometry for grasp optimization

Innovation

• Force-aware contact representation using discrete force levels
• Stability constraints defined via acceleration minimization task
• Pose optimizer integrating contact representation and stability constraints