AI 中文总结
本研究针对人形机器人执行建筑任务的实用能力不足问题，提出含Humanoid-PoseNet和Humanoid-ActionNet的感知动作系统，经实验实现8项建筑动作可靠执行，平均MPJPE为82.45mm，为建筑场景部署人形协作机器人奠定基础。
AI 中文摘要
人形机器人具有类人外形和多任务处理能力，非常适配土木和建筑工程等以人类为主导的工作场所，可与人类工人协作或自主执行体力要求高且危险的任务。尽管前景广阔，但关于赋予这类机器人执行建筑任务所需实用能力的研究有限。为此，本研究提出一种新型感知与动作系统，使人形机器人可从工人演示中学习并执行建筑任务。该系统包含两个深度网络：Humanoid-PoseNet，用于提取人类姿态并将其转换为人形机器人可机械实现的姿态；Humanoid-ActionNet，基于这些转换后的姿态学习机器人可执行的动作。实验结果表明，该人形机器人可靠执行了8项与建筑相关的动作，平均运动跟踪误差为82.45 mm MPJPE（Mean Per Joint Position Error）。本研究为在建筑领域部署人形协作机器人迈出了早期一步。
英文摘要
Humanoid robots, with their human-like shape and multi-tasking capabilities, are well-aligned with human-dominated workplaces, like those in civil and construction engineering, where they could collaborate with human workers or autonomously perform physically demanding and hazardous tasks. Despite this promise, limited research has explored how to endow these robots with the practical capabilities needed to perform construction tasks. To this end, this study proposes a novel perception-and-action system that enables humanoid robots to learn and perform construction tasks from worker demonstrations. This system contains two deep networks: Humanoid-PoseNet, which extracts human postures and translates them into mechanically feasible poses for a humanoid robot; and Humanoid-ActionNet, which learns robot-executable actions based on these translated poses. Experimental results demonstrate that the humanoid robot reliably executed eight construction-related actions, achieving an average motion-tracking error of 82.45 mm MPJPE (Mean Per Joint Position Error). This work provides an early step toward deploying humanoid collaborators in construction.
Journal refComputer-Aided Civil and Infrastructure Engineering, Volume 50, 100107, ISSN 1093-9687, 2026
DOI：10.1016/j.cacaie.2026.100107
