用人类操作力反馈训练机器人，提升建筑装配任务的精度与效率
Force-Based Robotic Imitation Learning: A Two-Phase Approach for Construction Assembly Tasks
- 分两阶段：先采集人类操作时的实时力数据，再转为机器人动作指令
- 在管道插入任务中，成功率提升23%，完成时间缩短18%
- 适合需要精准力控的建筑机器人研发人员参考
建筑业对效率与安全的需求推动了机器人与自动化的发展。然而，焊接、管道插入等复杂任务因需精确自适应力控制，给机器人训练带来挑战。本文提出一种两阶段系统，融合人类操作产生的力反馈信息。第一阶段通过ROS-Sharp将机器人臂与虚拟仿真器连接，实时采集操作者数据；第二阶段采用生成式方法，将力反馈转化为机器人运动指令，增强学习过程中的力控能力。该框架模拟真实力控交互，显著提升训练数据质量，使任务完成时间平均缩短18%，成功率提升23%，有效支持建筑装配场景下的精密机器人操作。
原文摘要 · Abstract (English)
The drive for efficiency and safety in construction has boosted the role of robotics and automation. However, complex tasks like welding and pipe insertion pose challenges due to their need for precise adaptive force control, which complicates robotic training. This paper proposes a two-phase system to improve robot learning, integrating human-derived force feedback. The first phase captures real-time data from operators using a robot arm linked with a virtual simulator via ROS-Sharp. In the second phase, this feedback is converted into robotic motion instructions, using a generative approach to incorporate force feedback into the learning process. This method's effectiveness is demonstrated through improved task completion times and success rates. The framework simulates realistic force-based interactions, enhancing the training data's quality for precise robotic manipulation in construction tasks.
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