TU Darmstadt / ULB / TUbiblio

Learning Trajectory Distributions for Assisted Teleoperation and Path Planning

Ewerton, Marco and Arenz, Oleg and Maeda, Guilherme and Koert, Dorothea and Kolev, Zlatko and Takahashi, Masaki and Peters, Jan (2019):
Learning Trajectory Distributions for Assisted Teleoperation and Path Planning.
In: Frontiers in Robotics and AI, 6, Frontiers, e-ISSN 2296-9144,
DOI: 10.25534/tuprints-00009657,
[Article]

Abstract

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.

Item Type: Article
Erschienen: 2019
Creators: Ewerton, Marco and Arenz, Oleg and Maeda, Guilherme and Koert, Dorothea and Kolev, Zlatko and Takahashi, Masaki and Peters, Jan
Origin: Secondary publication via sponsored Golden Open Access
Title: Learning Trajectory Distributions for Assisted Teleoperation and Path Planning
Language: English
Abstract:

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.

Journal or Publication Title: Frontiers in Robotics and AI
Journal volume: 6
Publisher: Frontiers
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Intelligent Autonomous Systems
Date Deposited: 15 Dec 2019 20:56
DOI: 10.25534/tuprints-00009657
Official URL: https://tuprints.ulb.tu-darmstadt.de/9657
URN: urn:nbn:de:tuda-tuprints-96572
Corresponding Links:
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details