Force-Based Robotic Imitation Learning: A Two-Phase Approach for Construction Assembly Tasks

TL;DR

This paper introduces a two-phase force-based imitation learning system for construction robots, enhancing their ability to perform complex tasks like welding and pipe insertion with improved accuracy and safety.

Contribution

It presents a novel two-phase framework that captures human force feedback and integrates it into robotic learning for construction tasks.

Findings

Improved task completion times

Higher success rates in construction tasks

Enhanced training data quality for robotic manipulation

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.