Object-Informed Model Predictive Path Integral Control for Non-Prehensile Robot Manipulation
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913172142686208 |
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| author | Raicevic, Nikola Radhakrishnan, Bharath Raam Yu, Chenbin Lee, Ki Myung Brian Atanasov, Nikolay |
| author_facet | Raicevic, Nikola Radhakrishnan, Bharath Raam Yu, Chenbin Lee, Ki Myung Brian Atanasov, Nikolay |
| contents | Long-horizon planning for non-prehensile robot manipulation is challenging due to underactuated and discontinuous interactions. We propose a hierarchical formulation of model predictive path integral (MPPI) control that guides robot-level planning with a separately computed object-level plan to achieve efficient long-horizon prediction. We first solve a simplified object-only problem, assuming the object can be actuated directly, and use the planned object trajectory as a reference in solving the joint robot-object planning problem. We evaluate our method in both simulation and hardware using a 6-DoF xArm6 manipulator to perform object pushing tasks in which the target object must reach a goal while avoiding static obstacles, necessitating non-myopic reasoning. Our object-informed MPPI increases task success by 40\% with a 26\% faster control frequency in simulation, and by 20\% in real experiments with similar computation as regular MPPI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30778 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Object-Informed Model Predictive Path Integral Control for Non-Prehensile Robot Manipulation Raicevic, Nikola Radhakrishnan, Bharath Raam Yu, Chenbin Lee, Ki Myung Brian Atanasov, Nikolay Robotics Long-horizon planning for non-prehensile robot manipulation is challenging due to underactuated and discontinuous interactions. We propose a hierarchical formulation of model predictive path integral (MPPI) control that guides robot-level planning with a separately computed object-level plan to achieve efficient long-horizon prediction. We first solve a simplified object-only problem, assuming the object can be actuated directly, and use the planned object trajectory as a reference in solving the joint robot-object planning problem. We evaluate our method in both simulation and hardware using a 6-DoF xArm6 manipulator to perform object pushing tasks in which the target object must reach a goal while avoiding static obstacles, necessitating non-myopic reasoning. Our object-informed MPPI increases task success by 40\% with a 26\% faster control frequency in simulation, and by 20\% in real experiments with similar computation as regular MPPI. |
| title | Object-Informed Model Predictive Path Integral Control for Non-Prehensile Robot Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.30778 |