Force-Based Robotic Imitation Learning: A Two-Phase Approach for Construction Assembly Tasks
Journal:
arXiv
Published Date:
Jan 24, 2025
Abstract
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.
