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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.14942 |
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| _version_ | 1866917902569963520 |
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| author | You, Hengxu Ye, Yang Zhou, Tianyu Du, Jing |
| author_facet | You, Hengxu Ye, Yang Zhou, Tianyu Du, Jing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14942 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Force-Based Robotic Imitation Learning: A Two-Phase Approach for Construction Assembly Tasks You, Hengxu Ye, Yang Zhou, Tianyu Du, Jing Robotics Artificial Intelligence 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. |
| title | Force-Based Robotic Imitation Learning: A Two-Phase Approach for Construction Assembly Tasks |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2501.14942 |