让四足机器人在负载下长时间稳定行走，同时防止电机过热。
Learning to Balance Motor Thermal Safety and Quadrupedal Locomotion Performance with Residual Policy
- 用热模型指导强化学习，分两阶段训练主策略和修正策略。
- 带3公斤负载时，实测持续行走超13分钟，原策略仅5分钟就过热。
- 适合需要长时间运行的机器人系统设计与热安全优化场景。
电机热管理在电动驱动机器人，尤其是足式机器人中常被忽视，但电机过热是限制长时间运动的关键因素，尤其在负载条件下。本文将四足机器人的全身热模型融入强化学习流程，用于动态更新电机温度，并提出一种两阶段训练框架。首先预训练一个基础运动策略，使其能适应多种地形；随后在该策略基础上训练一个残差策略，根据机器人的热状态提供修正动作，在低温时保持高性能，在高温时防止电机过热。仿真结果表明，该策略有效平衡了电机热安全与运动性能。真实世界实验在Unitree A1四足机器人上验证：搭载3公斤负载时，机器人可在多个地形上稳定行走超过13分钟，而仅使用基础策略则约5分钟即出现电机过热。
原文摘要 · Abstract (English)
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.
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