ToMPC: Task-oriented Model Predictive Control via ADMM for Safe Robotic Manipulation
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918388135100416 |
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| author | Jia, Xinyu Wang, Wenxin Yang, Jun Pan, Yongping Yu, Haoyong |
| author_facet | Jia, Xinyu Wang, Wenxin Yang, Jun Pan, Yongping Yu, Haoyong |
| contents | This paper proposes a task-oriented model predictive control (ToMPC) framework for safe and efficient robotic manipulation in open workspaces. The framework unifies collision-free motion and robot-environment interaction to address diverse scenarios. Additionally, it introduces task-oriented obstacle avoidance that leverages kinematic redundancy to enhance manipulation efficiency in obstructed environments. This complex optimization problem is solved by the alternating direction method of multipliers (ADMM), which decomposes the problem into two subproblems tackled by differential dynamic programming (DDP) and quadratic programming (QP), respectively. The effectiveness of this approach is validated in simulation and hardware experiments on a Franka Panda robotic manipulator. Results demonstrate that the framework can plan motion and/or force trajectories in real time, maximize the manipulation range while avoiding obstacles, and strictly adhere to safety-related hard constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13944 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ToMPC: Task-oriented Model Predictive Control via ADMM for Safe Robotic Manipulation Jia, Xinyu Wang, Wenxin Yang, Jun Pan, Yongping Yu, Haoyong Robotics This paper proposes a task-oriented model predictive control (ToMPC) framework for safe and efficient robotic manipulation in open workspaces. The framework unifies collision-free motion and robot-environment interaction to address diverse scenarios. Additionally, it introduces task-oriented obstacle avoidance that leverages kinematic redundancy to enhance manipulation efficiency in obstructed environments. This complex optimization problem is solved by the alternating direction method of multipliers (ADMM), which decomposes the problem into two subproblems tackled by differential dynamic programming (DDP) and quadratic programming (QP), respectively. The effectiveness of this approach is validated in simulation and hardware experiments on a Franka Panda robotic manipulator. Results demonstrate that the framework can plan motion and/or force trajectories in real time, maximize the manipulation range while avoiding obstacles, and strictly adhere to safety-related hard constraints. |
| title | ToMPC: Task-oriented Model Predictive Control via ADMM for Safe Robotic Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.13944 |