Whole-Body Inverse Dynamics MPC for Legged Loco-Manipulation
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908673286078464 |
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| author | Molnar, Lukas Cheng, Jin Fadini, Gabriele Kang, Dongho Zargarbashi, Fatemeh Coros, Stelian |
| author_facet | Molnar, Lukas Cheng, Jin Fadini, Gabriele Kang, Dongho Zargarbashi, Fatemeh Coros, Stelian |
| contents | Loco-manipulation demands coordinated whole-body motion to manipulate objects effectively while maintaining locomotion stability, presenting significant challenges for both planning and control. In this work, we propose a whole-body model predictive control (MPC) framework that directly optimizes joint torques through full-order inverse dynamics, enabling unified motion and force planning and execution within a single predictive layer. This approach allows emergent, physically consistent whole-body behaviors that account for the system's dynamics and physical constraints. We implement our MPC formulation using open software frameworks (Pinocchio and CasADi), along with the state-of-the-art interior-point solver Fatrop. In real-world experiments on a Unitree B2 quadruped equipped with a Unitree Z1 manipulator arm, our MPC formulation achieves real-time performance at 80 Hz. We demonstrate loco-manipulation tasks that demand fine control over the end-effector's position and force to perform real-world interactions like pulling heavy loads, pushing boxes, and wiping whiteboards. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Whole-Body Inverse Dynamics MPC for Legged Loco-Manipulation Molnar, Lukas Cheng, Jin Fadini, Gabriele Kang, Dongho Zargarbashi, Fatemeh Coros, Stelian Robotics Loco-manipulation demands coordinated whole-body motion to manipulate objects effectively while maintaining locomotion stability, presenting significant challenges for both planning and control. In this work, we propose a whole-body model predictive control (MPC) framework that directly optimizes joint torques through full-order inverse dynamics, enabling unified motion and force planning and execution within a single predictive layer. This approach allows emergent, physically consistent whole-body behaviors that account for the system's dynamics and physical constraints. We implement our MPC formulation using open software frameworks (Pinocchio and CasADi), along with the state-of-the-art interior-point solver Fatrop. In real-world experiments on a Unitree B2 quadruped equipped with a Unitree Z1 manipulator arm, our MPC formulation achieves real-time performance at 80 Hz. We demonstrate loco-manipulation tasks that demand fine control over the end-effector's position and force to perform real-world interactions like pulling heavy loads, pushing boxes, and wiping whiteboards. |
| title | Whole-Body Inverse Dynamics MPC for Legged Loco-Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.19709 |