MLM: Learning Multi-task Loco-Manipulation Whole-Body Control for Quadruped Robot with Arm
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866914152366211072 |
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| author | Liu, Xin Ma, Bida Qi, Chenkun Ding, Yan Xu, Nuo Zhaxizhuoma Zhang, Guorong Chen, Pengan Liu, Kehui Jia, Zhongjie Guan, Chuyue Mo, Yule Liu, Jiaqi Gao, Feng Zhong, Jiangwei Zhao, Bin Li, Xuelong |
| author_facet | Liu, Xin Ma, Bida Qi, Chenkun Ding, Yan Xu, Nuo Zhaxizhuoma Zhang, Guorong Chen, Pengan Liu, Kehui Jia, Zhongjie Guan, Chuyue Mo, Yule Liu, Jiaqi Gao, Feng Zhong, Jiangwei Zhao, Bin Li, Xuelong |
| contents | Whole-body loco-manipulation for quadruped robots with arms remains a challenging problem, particularly in achieving multi-task control. To address this, we propose MLM, a reinforcement learning framework driven by both real-world and simulation data. It enables a six-DoF robotic arm-equipped quadruped robot to perform whole-body loco-manipulation for multiple tasks autonomously or under human teleoperation. To address the problem of balancing multiple tasks during the learning of loco-manipulation, we introduce a trajectory library with an adaptive, curriculum-based sampling mechanism. This approach allows the policy to efficiently leverage real-world collected trajectories for learning multi-task loco-manipulation. To address deployment scenarios with only historical observations and to enhance the performance of policy execution across tasks with different spatial ranges, we propose a Trajectory-Velocity Prediction policy network. It predicts unobservable future trajectories and velocities. By leveraging extensive simulation data and curriculum-based rewards, our controller achieves whole-body behaviors in simulation and zero-shot transfer to real-world deployment. Ablation studies in simulation verify the necessity and effectiveness of our approach, while real-world experiments on a Go2 robot with an Airbot robotic arm demonstrate the policy's good performance in multi-task execution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10538 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MLM: Learning Multi-task Loco-Manipulation Whole-Body Control for Quadruped Robot with Arm Liu, Xin Ma, Bida Qi, Chenkun Ding, Yan Xu, Nuo Zhaxizhuoma Zhang, Guorong Chen, Pengan Liu, Kehui Jia, Zhongjie Guan, Chuyue Mo, Yule Liu, Jiaqi Gao, Feng Zhong, Jiangwei Zhao, Bin Li, Xuelong Robotics Whole-body loco-manipulation for quadruped robots with arms remains a challenging problem, particularly in achieving multi-task control. To address this, we propose MLM, a reinforcement learning framework driven by both real-world and simulation data. It enables a six-DoF robotic arm-equipped quadruped robot to perform whole-body loco-manipulation for multiple tasks autonomously or under human teleoperation. To address the problem of balancing multiple tasks during the learning of loco-manipulation, we introduce a trajectory library with an adaptive, curriculum-based sampling mechanism. This approach allows the policy to efficiently leverage real-world collected trajectories for learning multi-task loco-manipulation. To address deployment scenarios with only historical observations and to enhance the performance of policy execution across tasks with different spatial ranges, we propose a Trajectory-Velocity Prediction policy network. It predicts unobservable future trajectories and velocities. By leveraging extensive simulation data and curriculum-based rewards, our controller achieves whole-body behaviors in simulation and zero-shot transfer to real-world deployment. Ablation studies in simulation verify the necessity and effectiveness of our approach, while real-world experiments on a Go2 robot with an Airbot robotic arm demonstrate the policy's good performance in multi-task execution. |
| title | MLM: Learning Multi-task Loco-Manipulation Whole-Body Control for Quadruped Robot with Arm |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.10538 |