Modular Reinforcement Learning In Long-Horizon Manipulation Tasks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F24%3A00377216" target="_blank" >RIV/68407700:21730/24:00377216 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-72359-9_22" target="_blank" >https://doi.org/10.1007/978-3-031-72359-9_22</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-72359-9_22" target="_blank" >10.1007/978-3-031-72359-9_22</a>

Result language

angličtina

Original language name

Modular Reinforcement Learning In Long-Horizon Manipulation Tasks

Original language description

Recently, a number of reinforcement learning (RL) algorithms have been proposed in the area of robotic manipulation. As most of the current robotic benchmarks are focused on simple, non-diverse tasks such as the translation of objects within the scene, various singlepolicy algorithms are able to solve them with a high success rate. However, when a sequence of diverse subgoals is required (translation, rotation, 6DOF manipulation, trajectory following), the single-policy networks are shown to fail. In this work, we propose two modular multipolicy algorithms (MultiPPO2 and MultiACKTR) that improve diverse long-horizon tasks by adopting a separate policy for each skill that follows its own subgoal. We tested our algorithm in a virtual robotic simulator both on single and multi-step tasks requiring non-diverse (translation) skills and also diverse (translation, rotation and path following) skills. Both algorithms (MultiPPO2 and MultiACKTR) achieved similar performance as single-policy algorithms in the single-ste

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Artificial Neural Networks and Machine Learning – ICANN 2024 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part IX

ISBN

978-3-031-72356-8

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

14

Pages from-to

299-312

Publisher name

Springer, Cham

Place of publication

—

Event location

Lugano-Viganello

Event date

Sep 17, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001331898500022