Publication

Improving human-robot interaction effectiveness in human-robot collaborative object transportation using force prediction

Conference Article

Conference

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Edition

2023

Pages

7839-7845

Doc link

https://doi.org/10.1109/IROS55552.2023.10342517

File

Download the digital copy of the doc pdf document

Abstract

In this work, we analyse the use of a prediction of the human’s force in a Human-Robot collaborative object transportation task at a middle distance. We check that this force prediction can improve multiple parameters associated with effective Human-Robot Interaction (HRI) such as percep- tion of the robot’s contribution to the task, comfort or trust in the robot in a physical Human Robot Interaction (pHRI). We present a Deep Learning model that allows to predict the force that a human will exert in the next 1 s using as inputs the force previously exerted by the human, the robot’s velocity and environment information obtained from the robot’s LiDAR. Its success rate is up to 92.3% in testset and up to 89.1% in real experiments. We demonstrate that this force prediction, in addition to being able to be used directly to detect changes in the human’s intention, can be processed to obtain an estimate of the human’s desired trajectory. We have validated this approach with a user study involving 18 volunteers.

Categories

automation, feature extraction, learning (artificial intelligence), mobile robots, transport control.

Author keywords

Physical Human-Robot Interaction, Object Transportation, Human-in-the-Loop, Force Prediction

Scientific reference

J.E. Domínguez and A. Sanfeliu. Improving human-robot interaction effectiveness in human-robot collaborative object transportation using force prediction, 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2023, Detroit, MI, USA, pp. 7839-7845.