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Effect regulated projection of robot’s action space for production and prediction of manipulation primitives through learning progress and predictability based exploration

Title	Effect regulated projection of robot’s action space for production and prediction of manipulation primitives through learning progress and predictability based exploration
Author	Bugur, S., Öztop, Erhan, Nagai, Y., Ugur, E.
Publication Date:	2021-06
Publication Place	- IEEE
Subject	Intrinsic motivation, Learning progress, Sensorimotor development, Primitive formation.
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	2379-8920
Record ID	f4c8fc0b-8cf7-4048-8cb1-46ce24de855e
Library Location	Computer Science
Date	2021-06
Notes	Bogazici Research Fund ; Japan Science and Technology Agency
Sample Text	In this study, we propose an effective action parameter exploration mechanism that enables efficient discovery of robot actions through interacting with objects in a simulated table-top environment. For this, the robot organizes its action parameter space based on the generated effects in the environment and learns forward models for predicting consequences of its actions. Following the Intrinsic Motivation approach, the robot samples the action parameters from the regions that are expected to yield high learning progress (LP). In addition to the LP-based action sampling, our method uses a novel parameter space organization scheme to form regions that naturally correspond to qualitatively different action classes, which might be also called action primitives. The proposed method enabled the robot to discover a number of lateralized movement primitives and to acquire the capability of prediction the consequences of these primitives. Furthermore our results suggest the reasons behind the earlier development of grasp compared to push action in infants. Finally, our findings show some parallels with data from infant development where correspondence between action production and prediction is observed.
DOI	10.1109/TCDS.2019.2933900
Cilt	13

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Effect regulated projection of robot’s action space for production and prediction of manipulation primitives through learning progress and predictability based exploration

Author Bugur, S., Öztop, Erhan, Nagai, Y., Ugur, E.

Publication Date 2021-06

Publication Place - IEEE

Subject Intrinsic motivation, Learning progress, Sensorimotor development, Primitive formation.

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 2379-8920

Record ID f4c8fc0b-8cf7-4048-8cb1-46ce24de855e

Library Location Computer Science

Date 2021-06

Notes Bogazici Research Fund ; Japan Science and Technology Agency

Sample Text In this study, we propose an effective action parameter exploration mechanism that enables efficient discovery of robot actions through interacting with objects in a simulated table-top environment. For this, the robot organizes its action parameter space based on the generated effects in the environment and learns forward models for predicting consequences of its actions. Following the Intrinsic Motivation approach, the robot samples the action parameters from the regions that are expected to yield high learning progress (LP). In addition to the LP-based action sampling, our method uses a novel parameter space organization scheme to form regions that naturally correspond to qualitatively different action classes, which might be also called action primitives. The proposed method enabled the robot to discover a number of lateralized movement primitives and to acquire the capability of prediction the consequences of these primitives. Furthermore our results suggest the reasons behind the earlier development of grasp compared to push action in infants. Finally, our findings show some parallels with data from infant development where correspondence between action production and prediction is observed.

DOI 10.1109/TCDS.2019.2933900

Cilt 13