让人形机器人通过模仿工人动作完成建筑任务
Perception-and-action system for humanoid robot task execution in construction
- 用双神经网络从人体动作提取并转为机器人可执行姿态
- 在8项建筑任务中平均定位误差达82.45毫米
- 适合希望实现人形机器人协作施工的工程团队
人形机器人因其类人形态和多任务能力，与以人类为主的土木与建筑工地高度契合，可协同作业或自主完成繁重危险任务。然而，现有研究较少探索其执行建筑任务的实际能力。为此，本研究提出一种感知-动作系统，使机器人通过工人示范学习并执行建筑任务。该系统包含两个深度网络：Humanoid-PoseNet 用于提取人体姿态并转换为机械可行的机器人姿态；Humanoid-ActionNet 则基于转换后的姿态学习机器人可执行动作。实验表明，该系统使机器人可靠完成八项建筑相关动作，平均关节位置误差（MPJPE）为82.45毫米。这项工作为部署人形协作机器人于建筑场景迈出初步一步。
原文摘要 · Abstract (English)
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.
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