四足机器人无需外部传感即可自适应未知动态负载，实现崎岖地形稳定行走。
Beyond Robustness: Learning Unknown Dynamic Load Adaptation for Quadruped Locomotion on Rough Terrain
- 提出负载特性建模方法，通用表征未知负载动力学。
- 结合强化学习，在无外部传感下实现负载动态推断与稳定控制。
- 实测在崎岖地形上抗冲击、负重行走表现优于现有方法。
未知动态负载是四足机器人的重要实际应用场景，但带来三大挑战：如何通用建模负载动力学、如何在无外部传感条件下捕捉负载状态、如何实现机器人与负载的双向交互以保持稳定。本文提出一种通用负载特性建模方法，结合基于强化学习的步态控制技术，使机器人能够间接推断负载运动动态，实现负载稳定与自主适应。通过大量仿真对比实验验证，该方法在突发负载抵抗、负载稳定性及重载崎岖地形行走方面均优于现有方法。项目页：https://leixinjonaschang.github.io/leggedloadadapt.github.io/
原文摘要 · Abstract (English)
Unknown dynamic load carrying is one important practical application for quadruped robots. Such a problem is non-trivial, posing three major challenges in quadruped locomotion control. First, how to model or represent the dynamics of the load in a generic manner. Second, how to make the robot capture the dynamics without any external sensing. Third, how to enable the robot to interact with load handling the mutual effect and stabilizing the load. In this work, we propose a general load modeling approach called load characteristics modeling to capture the dynamics of the load. We integrate this proposed modeling technique and leverage recent advances in Reinforcement Learning (RL) based locomotion control to enable the robot to infer the dynamics of load movement and interact with the load indirectly to stabilize it and realize the sim-to-real deployment to verify its effectiveness in real scenarios. We conduct extensive comparative simulation experiments to validate the effectiveness and superiority of our proposed method. Results show that our method outperforms other methods in sudden load resistance, load stabilizing and locomotion with heavy load on rough terrain. \href{https://leixinjonaschang.github.io/leggedloadadapt.github.io/}{Project Page}.
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