Learning to Balance Motor Thermal Safety and Quadrupedal Locomotion Performance with Residual Policy
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917536489013248 |
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| author | Wan, Yuhang Lin, Weixian Qian, Letian Zou, Yiqi Wu, Weiwei Wu, Shengwei Zhao, Chuanlin Luo, Xin |
| author_facet | Wan, Yuhang Lin, Weixian Qian, Letian Zou, Yiqi Wu, Weiwei Wu, Shengwei Zhao, Chuanlin Luo, Xin |
| contents | Motor thermal management is often overlooked in the context of electrically-actuated robots, particularly legged robots, but motor overheating is a key factor that limits long-duration locomotion especially under payload conditions. This paper integrates a whole-body thermal model of a quadruped robot into the reinforcement learning pipeline to update motor temperatures, and proposes a two-stage training framework for motor thermal management. In this framework, a nominal policy is first pre-trained as a locomotion baseline capable of traversing diverse terrains. A residual policy is then trained on top of the nominal policy to provide corrective actions based on the robot's thermal state, ensuring high performance under low-temperature conditions and preventing motor overheating under high-temperature conditions. Simulation results demonstrate that the proposed policy achieves an effective balance between motor thermal safety and locomotion performance. Real-world experiments on a Unitree A1 quadruped robot further validate the approach: under a 3 kg payload, the robot achieves stable locomotion across multiple terrains for over 13 minutes, while the nominal policy alone leads to motor overheating in about 5 minutes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_27046 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning to Balance Motor Thermal Safety and Quadrupedal Locomotion Performance with Residual Policy Wan, Yuhang Lin, Weixian Qian, Letian Zou, Yiqi Wu, Weiwei Wu, Shengwei Zhao, Chuanlin Luo, Xin Robotics Motor thermal management is often overlooked in the context of electrically-actuated robots, particularly legged robots, but motor overheating is a key factor that limits long-duration locomotion especially under payload conditions. This paper integrates a whole-body thermal model of a quadruped robot into the reinforcement learning pipeline to update motor temperatures, and proposes a two-stage training framework for motor thermal management. In this framework, a nominal policy is first pre-trained as a locomotion baseline capable of traversing diverse terrains. A residual policy is then trained on top of the nominal policy to provide corrective actions based on the robot's thermal state, ensuring high performance under low-temperature conditions and preventing motor overheating under high-temperature conditions. Simulation results demonstrate that the proposed policy achieves an effective balance between motor thermal safety and locomotion performance. Real-world experiments on a Unitree A1 quadruped robot further validate the approach: under a 3 kg payload, the robot achieves stable locomotion across multiple terrains for over 13 minutes, while the nominal policy alone leads to motor overheating in about 5 minutes. |
| title | Learning to Balance Motor Thermal Safety and Quadrupedal Locomotion Performance with Residual Policy |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.27046 |