TU Darmstadt / ULB / TUbiblio

Demonstration based trajectory optimization for generalizable robot motions

Koert, Dorothea ; Maeda, Guilherme ; Lioutikov, Rudolf ; Neumann, Gerhard ; Peters, Jan (2022)
Demonstration based trajectory optimization for generalizable robot motions.
International Conference on Humanoid Robots (Humanoids). Cancun, Mexico (15.-17.11.2016)
doi: 10.26083/tuprints-00020544
Conference or Workshop Item, Secondary publication, Postprint

Abstract

Learning motions from human demonstrations provides an intuitive way for non-expert users to teach tasks to robots. In particular, intelligent robotic co-workers should not only mimic human demonstrations but should also be able to adapt them to varying application scenarios. As such, robots must have the ability to generalize motions to different workspaces, e.g. to avoid obstacles not present during original demonstrations. Towards this goal our work proposes a unified method to (1) generalize robot motions to different workspaces, using a novel formulation of trajectory optimization that explicitly incorporates human demonstrations, and (2) to locally adapt and reuse the optimized solution in the form of a distribution of trajectories. This optimized distribution can be used, online, to quickly satisfy via-points and goals of a specific task. We validate the method using a 7 degrees of freedom (DoF) lightweight arm that grasps and places a ball into different boxes while avoiding obstacles that were not present during the original human demonstrations.

Item Type: Conference or Workshop Item
Erschienen: 2022
Creators: Koert, Dorothea ; Maeda, Guilherme ; Lioutikov, Rudolf ; Neumann, Gerhard ; Peters, Jan
Type of entry: Secondary publication
Title: Demonstration based trajectory optimization for generalizable robot motions
Language: English
Date: 2022
Place of Publication: Darmstadt
Publisher: IEEE
Book Title: 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)
Collation: 8 Seiten
Event Title: International Conference on Humanoid Robots (Humanoids)
Event Location: Cancun, Mexico
Event Dates: 15.-17.11.2016
DOI: 10.26083/tuprints-00020544
URL / URN: https://tuprints.ulb.tu-darmstadt.de/20544
Corresponding Links:
Origin: Secondary publication service
Abstract:

Learning motions from human demonstrations provides an intuitive way for non-expert users to teach tasks to robots. In particular, intelligent robotic co-workers should not only mimic human demonstrations but should also be able to adapt them to varying application scenarios. As such, robots must have the ability to generalize motions to different workspaces, e.g. to avoid obstacles not present during original demonstrations. Towards this goal our work proposes a unified method to (1) generalize robot motions to different workspaces, using a novel formulation of trajectory optimization that explicitly incorporates human demonstrations, and (2) to locally adapt and reuse the optimized solution in the form of a distribution of trajectories. This optimized distribution can be used, online, to quickly satisfy via-points and goals of a specific task. We validate the method using a 7 degrees of freedom (DoF) lightweight arm that grasps and places a ball into different boxes while avoiding obstacles that were not present during the original human demonstrations.

Status: Postprint
URN: urn:nbn:de:tuda-tuprints-205443
Classification DDC: 000 Generalities, computers, information > 004 Computer science
600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Intelligent Autonomous Systems
TU-Projects: EC/H2020|640554|SKILLS4ROBOTS
Date Deposited: 18 Nov 2022 13:57
Last Modified: 21 Nov 2022 10:45
PPN:
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