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Exploration with intrinsic motivation using object–action–outcome latent space

Title	Exploration with intrinsic motivation using object–action–outcome latent space
Author	Sener, M. İ., Nagai, Y., Öztop, Erhan, Uğur, E.
Publication Date:	2023-06
Publication Place	- IEEE
Subject	Developmental robotics, Effect prediction, Intrinsic motivation (IM), Open-ended learning, Representation learning
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	2379-8920
Record ID	18b3ac6d-61cb-41c5-904f-ba8c0aa5c755
Library Location	Computer Science
Date	2023-06
Sample Text	One effective approach for equipping artificial agents with sensorimotor skills is to use self-exploration. To do this efficiently is critical, as time and data collection are costly. In this study, we propose an exploration mechanism that blends action, object, and action outcome representations into a latent space, where local regions are formed to host forward model learning. The agent uses intrinsic motivation to select the forward model with the highest learning progress to adopt at a given exploration step. This parallels how infants learn, as high learning progress indicates that the learning problem is neither too easy nor too difficult in the selected region. The proposed approach is validated with a simulated robot in a table-top environment. The simulation scene comprises a robot and various objects, where the robot interacts with one of them each time using a set of parameterized actions and learns the outcomes of these interactions. With the proposed approach, the robot organizes its curriculum of learning as in existing intrinsic motivation approaches and outperforms them in learning speed. Moreover, the learning regime demonstrates features that partially match infant development; in particular, the proposed system learns to predict the outcomes of different skills in a staged manner.
DOI	10.1109/TCDS.2021.3062728
Cilt	15

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Özyeğin University

Exploration with intrinsic motivation using object–action–outcome latent space

Author Sener, M. İ., Nagai, Y., Öztop, Erhan, Uğur, E.

Publication Date 2023-06

Publication Place - IEEE

Subject Developmental robotics, Effect prediction, Intrinsic motivation (IM), Open-ended learning, Representation learning

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 2379-8920

Record ID 18b3ac6d-61cb-41c5-904f-ba8c0aa5c755

Library Location Computer Science

Date 2023-06

Sample Text One effective approach for equipping artificial agents with sensorimotor skills is to use self-exploration. To do this efficiently is critical, as time and data collection are costly. In this study, we propose an exploration mechanism that blends action, object, and action outcome representations into a latent space, where local regions are formed to host forward model learning. The agent uses intrinsic motivation to select the forward model with the highest learning progress to adopt at a given exploration step. This parallels how infants learn, as high learning progress indicates that the learning problem is neither too easy nor too difficult in the selected region. The proposed approach is validated with a simulated robot in a table-top environment. The simulation scene comprises a robot and various objects, where the robot interacts with one of them each time using a set of parameterized actions and learns the outcomes of these interactions. With the proposed approach, the robot organizes its curriculum of learning as in existing intrinsic motivation approaches and outperforms them in learning speed. Moreover, the learning regime demonstrates features that partially match infant development; in particular, the proposed system learns to predict the outcomes of different skills in a staged manner.

DOI 10.1109/TCDS.2021.3062728

Cilt 15