Grasp-MPC: Closed-Loop Visual Grasping via Value-Guided Model Predictive Control
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arXiv
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| Format: | Preprint |
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2025
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| author | Yamada, Jun Murali, Adithyavairavan Mandlekar, Ajay Eppner, Clemens Posner, Ingmar Sundaralingam, Balakumar |
| author_facet | Yamada, Jun Murali, Adithyavairavan Mandlekar, Ajay Eppner, Clemens Posner, Ingmar Sundaralingam, Balakumar |
| contents | Grasping of diverse objects in unstructured environments remains a significant challenge. Open-loop grasping methods, effective in controlled settings, struggle in cluttered environments. Grasp prediction errors and object pose changes during grasping are the main causes of failure. In contrast, closed-loop methods address these challenges in simplified settings (e.g., single object on a table) on a limited set of objects, with no path to generalization. We propose Grasp-MPC, a closed-loop 6-DoF vision-based grasping policy designed for robust and reactive grasping of novel objects in cluttered environments. Grasp-MPC incorporates a value function, trained on visual observations from a large-scale synthetic dataset of 2 million grasp trajectories that include successful and failed attempts. We deploy this learned value function in an MPC framework in combination with other cost terms that encourage collision avoidance and smooth execution. We evaluate Grasp-MPC on FetchBench and real-world settings across diverse environments. Grasp-MPC improves grasp success rates by up to 32.6% in simulation and 33.3% in real-world noisy conditions, outperforming open-loop, diffusion policy, transformer policy, and IQL approaches. Videos and more at http://grasp-mpc.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06201 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Grasp-MPC: Closed-Loop Visual Grasping via Value-Guided Model Predictive Control Yamada, Jun Murali, Adithyavairavan Mandlekar, Ajay Eppner, Clemens Posner, Ingmar Sundaralingam, Balakumar Robotics Artificial Intelligence Machine Learning Grasping of diverse objects in unstructured environments remains a significant challenge. Open-loop grasping methods, effective in controlled settings, struggle in cluttered environments. Grasp prediction errors and object pose changes during grasping are the main causes of failure. In contrast, closed-loop methods address these challenges in simplified settings (e.g., single object on a table) on a limited set of objects, with no path to generalization. We propose Grasp-MPC, a closed-loop 6-DoF vision-based grasping policy designed for robust and reactive grasping of novel objects in cluttered environments. Grasp-MPC incorporates a value function, trained on visual observations from a large-scale synthetic dataset of 2 million grasp trajectories that include successful and failed attempts. We deploy this learned value function in an MPC framework in combination with other cost terms that encourage collision avoidance and smooth execution. We evaluate Grasp-MPC on FetchBench and real-world settings across diverse environments. Grasp-MPC improves grasp success rates by up to 32.6% in simulation and 33.3% in real-world noisy conditions, outperforming open-loop, diffusion policy, transformer policy, and IQL approaches. Videos and more at http://grasp-mpc.github.io. |
| title | Grasp-MPC: Closed-Loop Visual Grasping via Value-Guided Model Predictive Control |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.06201 |