Combining Task and Motion Planning using Policy Improvement with Path Integrals

Dominik Urbaniak, Alejandro Agostini, Dongheui Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Task and motion planning deals with complex tasks that require a robot to automatically define and execute multi-step sequences of actions in cluttered scenarios. In this context, a linear motion is often not sufficient to approach a target object since collisions of the gripper with other objects or the target object might occur. Thus, motion planners should be able to generate collision-free trajectories for every particular configuration of obstacles for grounding the symbolic actions of the task plan. Current approaches either search for feasible motions offline using computationally expensive trial-and-error processes on physically realistic simulations or learn a set of motion parameters for particular object configuration spaces with little generalization. This work proposes an appealing alternative by efficiently generating trajectories for the collisionfree execution of symbolic actions in variable scenarios without the need of intensive offline simulations. Our approach combines the benefit of learning from demonstration, to quickly generate an initial set of motion parameters for each symbolic action, with policy improvement with path integrals, to diversify this initial set of parameters to cope with different obstacle configurations.We show how the improved flexibility is achieved after a few minutes of training and successfully solves tasks requiring different sequences of picking and placing actions in variable configurations of obstacles.

Original languageEnglish
Title of host publication2020 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2020
EditorsTamim Asfour, Dongheui Lee, Mombaur Katja, Katsu Yamane, Kensuke Harada, Ludovic Righetti, Nikos Tsagarakis, Tomomichi Sugihara
PublisherIEEE Computer Society
Pages149-155
Number of pages7
ISBN (Electronic)9781728193724
DOIs
StatePublished - 2021
Externally publishedYes
Event20th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2020 - Munich, Germany
Duration: 19 Jul 202121 Jul 2021

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
Volume2021-July
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580

Conference

Conference20th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2020
Country/TerritoryGermany
CityMunich
Period19/07/2121/07/21

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