Neural Collision Detection for Constrained Grasp Pose Optimization in Cluttered Environments

Abstract

Robust robotic grasping in cluttered environments presents a significant challenge, as existing methods often neglect the complex interactions between the gripper, objects, and obstacles, leading to collisions and grasping failures. To address this, we propose a framework that integrates collision avoidance as a core constraint within the grasp pose optimization process. Central to this framework is a Neural Collision Detection (NCD) network that takes scene configurations and grasp poses as inputs, producing a collision score that approximates traditional collision detection functions. The NCD network provides critical feedback for refining grasp predictions and demonstrates strong generalization across diverse environments, facilitating efficient collision detection and constrained grasp pose optimization. Additionally, we incorporate frictional force closure, geometric symmetry, and surface alignment as regularization terms within the optimization function, enhancing the physical stability and geometric plausibility of the generated grasps. Extensive experiments conducted in real-world environments show a significant improvement in grasp success rates, with robust generalization to previously unseen objects and scenarios. These results validate the efficacy of our framework, highlighting its potential for enabling reliable robotic manipulation in complex and cluttered environments.