外观
论文arXiv 2610.2354812 天前
基于强化学习的四足机械臂多地形自适应全身控制
原文链接：Reinforcement Learning Based Multi-Terrain Adaptive Whole-Body Control for a Quadruped Manipulator（arXiv 2610.23548 · 发布日期：2026-10-16）
Reinforcement Learning Based Multi-Terrain Adaptive Whole-Body Control for a Quadruped Manipulator
针对复杂地形下四足机械臂末端任务一致性难题，提出强化学习全身控制框架，并在小型平台实地验证。
核心要点
- 地形扰动和运动-操作强耦合，使小型四足平台在行走中保持末端行为一致仍具挑战。
- 方法结合混合任务表示与任务优先全身学习，协调运动和操作并保持任务一致末端行为。
- 研究开发了轻量四足机械臂平台，用于验证所提框架在小型四足系统上的效果。
- 真实实验在最高30度斜坡、楼梯和可变形地形上成功部署，显示真实环境可行性。
验证范围与局限：验证范围为真实世界小型四足机械臂平台，地形包括最高30度斜坡、楼梯和可变形地形。
英文摘要 (Abstract)
Legged robots equipped with manipulators offer promising capabilities for loco-manipulation tasks in unstructured environments. However, achieving task-consistent end-effector behavior during locomotion remains challenging due to terrain-induced disturbances and the strong coupling between locomotion and manipulation, particularly for small-scale quadruped platforms. A reinforcement-learning based whole-body control framework is proposed for quadruped loco-manipulation on complex terrain. The proposed framework combines a hybrid task representation with task-prioritized whole-body learning to coordinate locomotion and manipulation while preserving task-consistent end-effector behavior. A lightweight quadruped manipulator platform is further developed to validate the proposed framework on a small-scale quadruped system. Real-world experiments demonstrate successful deployment on complex terrains such as slopes up to 30 degrees, stairs, and deformable terrain. These results demonstrate the feasibility of deploying learned whole-body control policy for quadruped loco-manipulation in real-world environments and highlight the importance of task-consistent whole-body coordination for terrain-adaptive loco-manipulation.
作者：Yanzhou Jin, Fei Suo, Chang Liu, Yasuhisa Hasegawa, Liming Chen, Yusuke Iwasawa, Yutaka Matsuo, Yaonan Zhu
来源
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