Electrically-actuated quadrupedal robots possess high mobility on complex terrains, but their motors tend to accumulate heat under high-torque cyclic loads, potentially triggering overheat protection and limiting long-duration tasks. This work proposes a thermal-aware control method that incorporates motor temperatures into reinforcement learning locomotion policies and introduces thermal-constraint rewards to prevent temperature exceedance. Real-world experiments on the Unitree A1 demonstrate that, under a fixed 3 kg payload, the baseline policy triggers overheat protection and stops within approximately 7 minutes, whereas the proposed method can operate continuously for over 27 minutes without thermal interruptions while maintaining comparable command-tracking performance, thereby enhancing sustainable operational capability.

翻译：电动驱动的四足机器人在复杂地形上具有高机动性，但其电机在高扭矩循环负载下易积聚热量，可能触发过热保护并限制长时程任务。本研究提出一种热感知控制方法，将电机温度纳入强化学习运动策略，并引入热约束奖励以防止温度超限。在宇树A1机器人上的真实世界实验表明，在固定3公斤负载下，基线策略约7分钟内即触发过热保护并停止运行，而所提方法可在保持相当指令跟踪性能的同时，持续运行超过27分钟且无热中断，从而显著提升了可持续作业能力。