Load-Aware Locomotion Control for Humanoid Robots in Industrial Transportation Tasks
This work addresses the problem of robust load-carrying for humanoid robots in industrial transportation, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the challenge of stable locomotion for humanoid robots carrying varying loads in industrial settings by introducing a load-aware locomotion framework that decouples and coordinates loco-manipulation. It demonstrates faster training, accurate height tracking, and stable performance in simulation and real-world experiments without fine-tuning.
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/