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.

翻译：电机热管理在电驱动机器人（尤其是腿足机器人）的研究中常被忽视，然而电机过热是限制机器人长时间运动的关键因素，特别是在负载条件下。本文将四足机器人的全身热模型集成到强化学习框架中以更新电机温度，并提出了一种用于电机热管理的两阶段训练框架。在该框架中，首先预训练一个名义策略作为能穿越多种地形的运动基线，随后在名义策略之上训练一个残差策略，根据机器人的热状态提供修正动作，确保其在低温条件下保持高性能，在高温条件下防止电机过热。仿真结果表明，所提出的策略能够有效平衡电机热安全与运动性能。在宇树A1四足机器人上的真实世界实验进一步验证了该方法：在3千克负载下，机器人可在多种地形上稳定运动超过13分钟，而仅使用名义策略会导致约5分钟内电机过热。