面向工业运输任务的人形机器人负载感知运动控制
Load-Aware Locomotion Control for Humanoid Robots in Industrial Transportation Tasks
AI总结：
本文提出一种负载感知的运动框架，通过解耦但协调的运动- manipulation架构，实现人形机器人在负载变化和上身运动下的稳定运动控制。
摘要：
在工业环境中部署的人形机器人需要执行紧密耦合的负载运输任务，但因动态耦合和部分可观测性，实现稳定和鲁棒的运动具有挑战性。本文提出基于解耦但协调的运动- manipulation架构的负载感知运动框架。下肢运动通过强化学习策略生成残差关节动作，基于运动学推导的名义配置。基于运动学的运动参考结合高度条件的关节空间偏移指导学习，而基于历史的状态估计器推断基底线速度和高度，并在紧凑的潜在表示中编码残差负载和 manipulation引起的扰动。该框架在仿真中完全训练，并在全尺寸人形机器人上部署而无需微调。仿真和现实实验表明训练更快，高度跟踪更准确，运动- manipulation更稳定。项目页面：https://lequn-f.github.io/LALO/
英文摘要：
Humanoid robots deployed in industrial environments are required to perform load-carrying transportation tasks that tightly couple locomotion and manipulation. However, achieving stable and robust locomotion under varying payloads and upper-body motions is challenging due to dynamic coupling and partial observability. This paper presents a load-aware locomotion framework for industrial humanoids based on a decoupled yet coordinated loco-manipulation architecture. Lower-body locomotion is controlled via a reinforcement learning policy producing residual joint actions on kinematically derived nominal configurations. A kinematics-based locomotion reference with a height-conditioned joint-space offset guides learning, while a history-based state estimator infers base linear velocity and height and encodes residual load- and manipulation-induced disturbances in a compact latent representation. The framework is trained entirely in simulation and deployed on a full-size humanoid robot without fine-tuning. Simulation and real-world experiments demonstrate faster training, accurate height tracking, and stable loco-manipulation. Project page: https://lequn-f.github.io/LALO/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
