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
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Authors
Projects associated
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.
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