Whole-Body Model-Predictive Control of Legged Robots with MuJoCo
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912946575114240 |
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| author | Zhang, John Z. Howell, Taylor A. Yi, Zeji Pan, Chaoyi Shi, Guanya Qu, Guannan Erez, Tom Tassa, Yuval Manchester, Zachary |
| author_facet | Zhang, John Z. Howell, Taylor A. Yi, Zeji Pan, Chaoyi Shi, Guanya Qu, Guannan Erez, Tom Tassa, Yuval Manchester, Zachary |
| contents | We demonstrate the surprising real-world effectiveness of a very simple approach to whole-body model-predictive control (MPC) of quadruped and humanoid robots: the iterative LQR (iLQR) algorithm with MuJoCo dynamics and finite-difference approximated derivatives. Building upon the previous success of model-based behavior synthesis and control of locomotion and manipulation tasks with MuJoCo in simulation, we show that these policies can easily generalize to the real world with few sim-to-real considerations. Our baseline method achieves real-time whole-body MPC on a variety of hardware experiments, including dynamic quadruped locomotion, quadruped walking on two legs, and full-sized humanoid bipedal locomotion. We hope this easy-to-reproduce hardware baseline lowers the barrier to entry for real-world whole-body MPC research and contributes to accelerating research velocity in the community. Our code and experiment videos will be available online at:https://johnzhang3.github.io/mujoco_ilqr |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04613 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Whole-Body Model-Predictive Control of Legged Robots with MuJoCo Zhang, John Z. Howell, Taylor A. Yi, Zeji Pan, Chaoyi Shi, Guanya Qu, Guannan Erez, Tom Tassa, Yuval Manchester, Zachary Robotics Systems and Control We demonstrate the surprising real-world effectiveness of a very simple approach to whole-body model-predictive control (MPC) of quadruped and humanoid robots: the iterative LQR (iLQR) algorithm with MuJoCo dynamics and finite-difference approximated derivatives. Building upon the previous success of model-based behavior synthesis and control of locomotion and manipulation tasks with MuJoCo in simulation, we show that these policies can easily generalize to the real world with few sim-to-real considerations. Our baseline method achieves real-time whole-body MPC on a variety of hardware experiments, including dynamic quadruped locomotion, quadruped walking on two legs, and full-sized humanoid bipedal locomotion. We hope this easy-to-reproduce hardware baseline lowers the barrier to entry for real-world whole-body MPC research and contributes to accelerating research velocity in the community. Our code and experiment videos will be available online at:https://johnzhang3.github.io/mujoco_ilqr |
| title | Whole-Body Model-Predictive Control of Legged Robots with MuJoCo |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2503.04613 |