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

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/

Figures (6)

• Figure 1: Overview of the proposed load-aware humanoid loco-manipulation framework. Upper-body manipulation is generated by perception-driven kinematic control using 6D object pose estimation, while lower-body locomotion is governed by a residual reinforcement learning policy with height-conditioned offsets and history-based state estimation. Historical proprioceptive observations are encoded by a history-based state estimator to infer base velocity, base height, and a compact latent state capturing load- and manipulation-induced disturbances.
• Figure 2: Multi-box detection and target 6D pose estimation. Blue boxes denote instances detected by YOLO-seg, the red box indicates the selected target, and the green wireframe shows the 6D pose estimated by FoundationPose.
• Figure 3: Training convergence comparison of different offset and reference formulations. (a) Episode reward for tracking base height versus environment steps. (b) Mean episode length versus environment steps.
• Figure 4: Distribution of joint residual actions during steady-state walking.
• Figure 5: Prediction performance of the history-based state estimator. Comparison between commanded, ground-truth, and estimated states for (a) base velocity $v_x$, (b) base velocity $v_y$, and (c) base height. The estimator achieves low mean squared error under dynamic motion conditions.