Load-Aware Locomotion Control for Humanoid Robots in Industrial Transportation Tasks

• URL: http://arxiv.org/abs/2603.14308v1
• Date: Sun, 15 Mar 2026 09:57:32 GMT
• Title: Load-Aware Locomotion Control for Humanoid Robots in Industrial Transportation Tasks
• Abstract summary: This paper presents a load-aware locomotion framework for industrial humanoids based on a decoupled yet coordinated loco-manipulation architecture.<n>Lower-body locomotion is controlled via a reinforcement learning policy producing residual joint actions on kinematically derived nominal configurations.<n>The framework is trained entirely in simulation and deployed on a full-size humanoid robot without fine-tuning.
• Score: 19.55626538861657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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