Applied Vision

GraspGen+HSR: Adapting Simulation-Trained 6-DoF Grasping to Real Service Robots Without Retraining

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Abstract

Recent diffusion-based 6-DoF grasp generation methods like GraspGen achieve state-of-the-art performance in simulation but face significant challenges when deployed on real robotic platforms. We present a unified adaptation pipeline for the Toyota Human Support Robot (HSR) that bridges these gaps without retraining the foundation model. Our approach combines symmetry-based point cloud completion to mitigate self-occlusion artifacts, three geometric feasibility filters that reduce motion planning failures from 66 % to 16 %, and a kinematic compensation for the HSR’s arc-shaped gripper trajectory. We show in our experiments, that our pipeline achieves an overall success rate of 85 % which is competitive with simulation of GraspGen while outperforming baselines M2T2 (56 %) and  AnyGrasp (70 %) by up to 29 percentage points. Ablation studies confirm the necessity of each component: symmetry completion improves success by +13 percentage points, while geometric filtering enables 4× more grasp candidates to reach execution. These results demonstrate that post-hoc adaptations can unlock the real-world potential of simulation-trained grasping foundation models on diverse hardware platforms. The code and repository are available at:
https://github.com/Ziegenschmuggler/GraspGenforHSR

How to Cite:

Dvorak, A., Nowak, M., Pulli, T. & Vincze, M., (2026) “GraspGen+HSR: Adapting Simulation-Trained 6-DoF Grasping to Real Service Robots Without Retraining”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision 3(1), 97-106.