Adapting to Human Preferences to Lead or Follow in Human-Robot Collaboration: A System Evaluation

TL;DR

This paper evaluates a system enabling humans and robots to adaptively choose to lead or follow in collaborative tasks, demonstrating the robot's ability to respond to different human behaviors in real scenarios.

Contribution

It introduces a task planning system that allows both humans and robots to select and assign tasks dynamically based on preferences, extending previous simulations to real-world implementation.

Findings

Robot adapts to different human leader/follower behaviors

System responds appropriately to enacted human behaviors

Preliminary results support system flexibility in real scenarios

Abstract

With the introduction of collaborative robots, humans and robots can now work together in close proximity and share the same workspace. However, this collaboration presents various challenges that need to be addressed to ensure seamless cooperation between the agents. This paper focuses on task planning for human-robot collaboration, taking into account the human's performance and their preference for following or leading. Unlike conventional task allocation methods, the proposed system allows both the robot and human to select and assign tasks to each other. Our previous studies evaluated the proposed framework in a computer simulation environment. This paper extends the research by implementing the algorithm in a real scenario where a human collaborates with a Fetch mobile manipulator robot. We briefly describe the experimental setup, procedure and implementation of the planned user study. As a first step, in this paper, we report on a system evaluation study where the experimenter enacted different possible behaviours in terms of leader/follower preferences that can occur in a user study. Results show that the robot can adapt and respond appropriately to different human agent behaviours, enacted by the experimenter. A future user study will evaluate the system with human participants.