让人形机器人在搬货时稳定行走，自动感知负载变化并调整动作。
Load-Aware Locomotion Control for Humanoid Robots in Industrial Transportation Tasks
- 分层控制：底层用强化学习学残差动作，上层用运动学参考引导行走姿态。
- 能精准跟踪高度变化，真实机器人实验中无需微调直接部署。
- 适合工业场景中需搬运重物的复杂行走任务，尤其关注稳定性与鲁棒性。
在工业环境中部署的人形机器人需完成负载运输任务，该任务紧密耦合行走与操作。然而，由于动态耦合和观测不全，不同负载和上肢动作下保持稳定行走极具挑战。本文提出一种基于解耦协同结构的负载感知行走框架：下肢通过强化学习策略生成基于运动学名义配置的残差关节动作；运动学引导的行走参考结合高度条件的关节空间偏移，辅助学习；基于历史的状态估计器推断基座线速度与高度，并将负载及操作引起的扰动编码为紧凑隐状态。整个框架完全在仿真中训练，直接部署于全尺寸人形机器人而无需微调。仿真与真实实验表明，该方法训练更快、高度跟踪更准，且实现稳定协同行走与操作。项目页面：https://lequn-f.github.io/LALO/
原文摘要 · Abstract (English)
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/
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