On the sensory commutativity of action sequences for embodied agents

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Abstract

We study perception in the scenario of an embodied agent equipped with first-person sensors and a continuous motor space with multiple degrees of freedom. We consider the commutative properties of action sequences with respect to sensory information perceived by such an embodied agent. We introduce the Sensory Commutativity Probability (SCP) criterion which measures how much an agent's degree of freedom affects the environment in embodied scenarios. We show how to compute this criterion in different environments, including realistic robotic setups and discuss how SCP and the commutative properties of action sequences can be used to learn about objects in the environment and improve sample-efficiency in Reinforcement Learning.

Original languageEnglish
Title of host publication20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1460-1462
Number of pages3
ISBN (Electronic)9781713832621
Publication statusPublished - 1 Jan 2021
Externally publishedYes
Event20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021 - Virtual, Online
Duration: 3 May 20217 May 2021

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume3
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021
CityVirtual, Online
Period3/05/217/05/21

Keywords

  • Artificial Perception
  • Robotics
  • Unsupervised Learning

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