RAMBO: RL-Augmented Model-Based Whole-Body Control for Loco-Manipulation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916882901106688 |
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| author | Cheng, Jin Kang, Dongho Fadini, Gabriele Shi, Guanya Coros, Stelian |
| author_facet | Cheng, Jin Kang, Dongho Fadini, Gabriele Shi, Guanya Coros, Stelian |
| contents | Loco-manipulation, physical interaction of various objects that is concurrently coordinated with locomotion, remains a major challenge for legged robots due to the need for both precise end-effector control and robustness to unmodeled dynamics. While model-based controllers provide precise planning via online optimization, they are limited by model inaccuracies. In contrast, learning-based methods offer robustness, but they struggle with precise modulation of interaction forces. We introduce RAMBO, a hybrid framework that integrates model-based whole-body control within a feedback policy trained with reinforcement learning. The model-based module generates feedforward torques by solving a quadratic program, while the policy provides feedback corrective terms to enhance robustness. We validate our framework on a quadruped robot across a diverse set of real-world loco-manipulation tasks, such as pushing a shopping cart, balancing a plate, and holding soft objects, in both quadrupedal and bipedal walking. Our experiments demonstrate that RAMBO enables precise manipulation capabilities while achieving robust and dynamic locomotion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06662 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RAMBO: RL-Augmented Model-Based Whole-Body Control for Loco-Manipulation Cheng, Jin Kang, Dongho Fadini, Gabriele Shi, Guanya Coros, Stelian Robotics Loco-manipulation, physical interaction of various objects that is concurrently coordinated with locomotion, remains a major challenge for legged robots due to the need for both precise end-effector control and robustness to unmodeled dynamics. While model-based controllers provide precise planning via online optimization, they are limited by model inaccuracies. In contrast, learning-based methods offer robustness, but they struggle with precise modulation of interaction forces. We introduce RAMBO, a hybrid framework that integrates model-based whole-body control within a feedback policy trained with reinforcement learning. The model-based module generates feedforward torques by solving a quadratic program, while the policy provides feedback corrective terms to enhance robustness. We validate our framework on a quadruped robot across a diverse set of real-world loco-manipulation tasks, such as pushing a shopping cart, balancing a plate, and holding soft objects, in both quadrupedal and bipedal walking. Our experiments demonstrate that RAMBO enables precise manipulation capabilities while achieving robust and dynamic locomotion. |
| title | RAMBO: RL-Augmented Model-Based Whole-Body Control for Loco-Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2504.06662 |