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

Lequn Fu1, Yijun Zhong1, Xiao Li1, Yibin Liu1, Zhiyuan Xu2, Jian Tang2, Shiqi Li1,†
1Huazhong University of Science and Technology
2Beijing Innovation Center of Humanoid Robotics
†Indicates Corresponding Author
Paper Code(Coming Soon) arXiv

Overview

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.

Method Overview

Paper page 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.

Velocity-Commanded Lower-Body References

Upper-Body Disturbance Simulation in Isaac Gym

Box Segmentation & 6D Object Pose Estimation

Depalletizing Task

BibTeX

@misc{fu2026loadaware,
  title         = {Load-Aware Locomotion Control for Humanoid Robots in Industrial Transportation Tasks},
  author        = {Lequn Fu and Yijun Zhong and Xiao Li and Yibin Liu and Zhiyuan Xu and Jian Tang and Shiqi Li},
  year          = {2026},
  eprint        = {2603.14308},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url           = {https://arxiv.org/abs/2603.14308}
}