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Learning Trajectory Distributions for Assisted Teleoperation and Path Planning

Ewerton, Marco ; Arenz, Oleg ; Maeda, Guilherme ; Koert, Dorothea ; Kolev, Zlatko ; Takahashi, Masaki ; Peters, Jan (2019)
Learning Trajectory Distributions for Assisted Teleoperation and Path Planning.
In: Frontiers in Robotics and AI, 2019, 6
doi: 10.25534/tuprints-00009657
Article, Secondary publication, Publisher's Version

Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

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Item Type: Article
Type of entry: Secondary publication
Title: Learning Trajectory Distributions for Assisted Teleoperation and Path Planning
Language: English
Date: 9 December 2019
Place of Publication: Darmstadt
Year of primary publication: 2019
Publisher: Frontiers
Journal or Publication Title: Frontiers in Robotics and AI
Volume of the journal: 6
DOI: 10.25534/tuprints-00009657
Corresponding Links:
Origin: Secondary publication via sponsored Golden Open Access

Several approaches have been proposed to assist humans in co-manipulation and teleoperation tasks given demonstrated trajectories. However, these approaches are not applicable when the demonstrations are suboptimal or when the generalization capabilities of the learned models cannot cope with the changes in the environment. Nevertheless, in real co-manipulation and teleoperation tasks, the original demonstrations will often be suboptimal and a learning system must be able to cope with new situations. This paper presents a reinforcement learning algorithm that can be applied to such problems. The proposed algorithm is initialized with a probability distribution of demonstrated trajectories and is based on the concept of relevance functions. We show in this paper how the relevance of trajectory parameters to optimization objectives is connected with the concept of Pearson correlation. First, we demonstrate the efficacy of our algorithm by addressing the assisted teleoperation of an object in a static virtual environment. Afterward, we extend this algorithm to deal with dynamic environments by utilizing Gaussian Process regression. The full framework is applied to make a point particle and a 7-DoF robot arm autonomously adapt their movements to changes in the environment as well as to assist the teleoperation of a 7-DoF robot arm in a dynamic environment.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-96572
Classification DDC: 000 Generalities, computers, information > 004 Computer science
Divisions: 20 Department of Computer Science > Intelligent Autonomous Systems
Date Deposited: 09 Dec 2019 13:46
Last Modified: 06 Dec 2023 07:25
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/9657
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