让机器人在复杂地形中边走边操作,还能提前感知地面变化。
Learning Terrain-Aware Whole-Body Control for Perceptive Legged Loco-Manipulation

- 用强化学习统一控制腿和手臂,结合视觉感知地形特征。
- 实测可减少摔倒次数,提升操作空间与定位精度。
- 适合需要在不平地面灵活作业的机器人研究者。
足式机械臂结合了出色的地形适应能力与移动操作功能,适用于人机交互环境。通过协调腿和臂的控制,全身控制器能显著扩展其操作空间。然而,现有全身控制器多依赖本体感知,缺乏对环境拓扑感知所需的外部感知能力,限制了其在复杂地形中的适应性。本文提出一种地形感知型全身控制框架TA-WBC,采用基于强化学习的统一策略,实现多种地形下的全身运动-操作任务。引入混合外部感知编码器提取地形特征,使机器人能主动调整姿态与落脚点;设计基于足接触平面的末端执行器采样方法,解耦操作目标与基座扰动;提出双策略蒸馏模块,在保留广泛运动能力的同时避免灾难性遗忘。仿真与真实实验验证了该控制器的鲁棒性,可实现更大可达空间、更低跟踪误差及更少意外失稳现象。该统一策略展现了足式机械臂在复杂地形中执行运动-操作任务的巨大潜力。
原文摘要 · Abstract (English)
Legged manipulators integrate exceptional terrain adaptability along with mobile manipulation capabilities, which make them highly promising for deployment in human-centric environments. By coordinating the control of both legs and arms, a whole-body controller can significantly expand the operational workspace of legged manipulators. However, many existing whole-body controllers primarily depend on proprioception and do not incorporate the critical exteroception required for effective terrain topology perception. This limitation can hinder their ability to adapt to varying environmental conditions and navigate complex terrains effectively. In this paper, we introduce TA-WBC, a terrain-aware whole-body control framework for legged manipulators, which features a novel RL-based unified policy tailored to whole-body loco-manipulation tasks in various terrains. Specifically, we employ a hybrid exteroception encoder to extract terrain features, providing an essential basis for the robot to proactively adapt posture and footholds. Furthermore, to facilitate stable cross-terrain loco-manipulation, we propose a novel end-effector sampling method based on the foot contact plane, decoupling manipulation target from base fluctuations. Moreover, a dual-policy distillation module is introduced to integrate expansive whole-body motion with terrain adaptability without catastrophic forgetting. The simulation and real-world experiments validate the robustness of our proposed controller, which leads to a larger reachable space, less tracking error, and reduced unexpected stumbles. This unified policy highlights the promising capabilities of legged manipulators in performing loco-manipulation tasks across complex terrains.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。