TU Darmstadt / ULB / TUprints

Assisting Movement Training and Execution With Visual and Haptic Feedback

Ewerton, Marco ; Rother, David ; Weimar, Jakob ; Kollegger, Gerrit ; Wiemeyer, Josef ; Peters, Jan ; Maeda, Guilherme (2018)
Assisting Movement Training and Execution With Visual and Haptic Feedback.
In: Frontiers in Neurorobotics, 2018, 12
Article, Secondary publication, Publisher's Version

[img]
Preview
Text
Ewerton.pdf
Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

Download (3MB) | Preview
Item Type: Article
Type of entry: Secondary publication
Title: Assisting Movement Training and Execution With Visual and Haptic Feedback
Language: English
Date: 10 July 2018
Place of Publication: Darmstadt
Year of primary publication: 2018
Publisher: Frontiers
Journal or Publication Title: Frontiers in Neurorobotics
Volume of the journal: 12
Corresponding Links:
Origin: Secondary publication via sponsored Golden Open Access
Abstract:

In the practice of motor skills in general, errors in the execution of movements may go unnoticed when a human instructor is not available. In this case, a computer system or robotic device able to detectmovement errors and propose corrections would be of great help. This paper addresses the problem of how to detect such execution errors and how to provide feedback to the human to correct his/her motor skill using a general, principled methodology based on imitation learning. The core idea is to compare the observed skill with a probabilistic model learned from expert demonstrations. The intensity of the feedback is regulated by the likelihood of the model given the observed skill. Based on demonstrations, our system can, for example, detect errors in the writing of characters with multiple strokes. Moreover, by using a haptic device, the Haption Virtuose 6D, we demonstrate a method to generate haptic feedback based on a distribution over trajectories, which could be used as an auxiliary means of communication between an instructor and an apprentice. Additionally, given a performance measurement, the haptic device can help the human discover and performbettermovements to solve a given task. In this case, the human first tries a few times to solve the task without assistance. Our framework, in turn, uses a reinforcement learning algorithm to compute haptic feedback, which guides the human toward better solutions.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-75662
Classification DDC: 600 Technology, medicine, applied sciences > 600 Technology
Divisions: 20 Department of Computer Science
Date Deposited: 10 Jul 2018 14:51
Last Modified: 05 Dec 2023 09:08
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/7566
PPN:
Export:
Actions (login required)
View Item View Item