# Rebellion and Obedience: The Effects of Intention Prediction in Cooperative Handheld Robots

## TL;DR

This paper presents a real-time intention prediction model for handheld robots using gaze tracking, improving cooperation by enabling the robot to anticipate user actions up to 1.5 seconds beforehand.

## Contribution

It introduces a gaze-based intention inference model for handheld robots, enhancing cooperative manipulation by predicting user actions in real-time.

## Key findings

- Model achieves reliable accuracy up to 1.5 seconds prior to action.
- Intention obedience improves cooperation in pick and place tasks.
- Rebellion and obedience dynamics affect task performance.

## Abstract

Within this work, we explore intention inference for user actions in the context of a handheld robot setup. Handheld robots share the shape and properties of handheld tools while being able to process task information and aid manipulation. Here, we propose an intention prediction model to enhance cooperative task solving. The model derives intention from the user's gaze pattern which is captured using a robot-mounted remote eye tracker. The proposed model yields real-time capabilities and reliable accuracy up to 1.5s prior to predicted actions being executed. We assess the model in an assisted pick and place task and show how the robot's intention obedience or rebellion affects the cooperation with the robot.