@article{Hossain2026, 
author = {Alamgir Hossain and Dingyuan Huang and Weizi Li and Jindong Tan and Shuai Li},
title = {Learning humanoid locomotion skills for material handling and transportation in construction sites},
year = {2026},
journal = {Journal of Intelligent Construction},
volume = {4},
number = {1},
pages = {9180110},
keywords = {construction robotics, humanoid robot, reinforcement learning, payload transportation},
url = {https://www.sciopen.com/article/10.26599/JIC.2026.9180110},
doi = {10.26599/JIC.2026.9180110},
abstract = {The increasing demand for automation in construction necessitates robotic solutions that can address labor shortages, safety concerns, and efficiency challenges. Humanoid robots, with their human-like form factor, are particularly well-suited for traversing and operating in human-centric construction environments. We presented an integrated humanoid robotic framework designed for robust locomotion and payload transportation across diverse construction surfaces. Our system combines dynamic bipedal walking with effective payload-carrying mechanisms, enabling seamless transitions between surfaces such as flat ground, ramps, stairs, narrow beams, random obstacles, and irregular trenches while carrying loads of up to 9 lb. We employed a unified training approach using reinforcement learning to achieve stable locomotion and real-time adaptation of walking styles based on the surface conditions. Experimental results demonstrate that our framework enables continuous locomotion at speeds up to 0.42 m/s across complex construction environments, showcasing the potential of humanoid robots in construction logistics.}
}