Proactive Human-Robot Co-Assembly: Leveraging Human Intention Prediction and Robust Safe Control

TL;DR

This paper introduces an integrated framework for proactive human-robot collaboration that predicts human intentions and ensures safety, significantly improving efficiency and safety in co-assembly tasks.

Contribution

It presents a novel combination of intention prediction and robust safe control to enhance proactive collaboration in HRC, addressing data scarcity and safety challenges.

Findings

Improves task efficiency by 15-20%.

Ensures interactive safety during collaboration.

Robust to environmental and behavioral variations.

Abstract

Human-robot collaboration (HRC) is one key component to achieving flexible manufacturing to meet the different needs of customers. However, it is difficult to build intelligent robots that can proactively assist humans in a safe and efficient way due to several challenges. First, it is challenging to achieve efficient collaboration due to diverse human behaviors and data scarcity. Second, it is difficult to ensure interactive safety due to uncertainty in human behaviors. This paper presents an integrated framework for proactive HRC. A robust intention prediction module, which leverages prior task information and human-in-the-loop training, is learned to guide the robot for efficient collaboration. The proposed framework also uses robust safe control to ensure interactive safety under uncertainty. The developed framework is applied to a co-assembly task using a Kinova Gen3 robot. The experiment demonstrates that our solution is robust to environmental changes as well as different human preferences and behaviors. In addition, it improves task efficiency by approximately 15-20%. Moreover, the experiment demonstrates that our solution can guarantee interactive safety during proactive collaboration.