A Hybrid Optimization Framework for Grasp Synthesis under Partial Observations

We propose a hybrid grasp synthesis framework that combines a learning-based Energy-Based Model (EBM) with an analytical Iterative Closest Point (ICP) method to generate robust grasps from partially observed point clouds. The learned energy function acts as a prior within a Stein Variational Gradient Descent (SVGD) framework, guiding iterative refinement of grasp configurations. Evaluated on 67 objects with 5,360 grasp attempts, our method achieves an average success rate of 60.9%, outperforming AnyGrasp (31.1%) and Grasp Pose Detection (48.4%) and AS-ICP (56.6%). These results highlight the strong generalization ability of our approach and demonstrate how combining data-driven learning with geometric optimization addresses the limitations of either strategy in isolation.

Source: arXiv:2606.18053v1 - http://arxiv.org/abs/2606.18053v1 PDF: https://arxiv.org/pdf/2606.18053v1 Original Link: http://arxiv.org/abs/2606.18053v1