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

Name: 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.

İsim	Effect regulated projection of robot’s action space for production and prediction of manipulation primitives through learning progress and predictability based exploration
Yazar	Bugur, S., Öztop, Erhan, Nagai, Y., Ugur, E.
Basım Tarihi:	2021-06
Basım Yeri	- IEEE
Konu	Intrinsic motivation, Learning progress, Sensorimotor development, Primitive formation.
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2379-8920
Kayıt Numarası	f4c8fc0b-8cf7-4048-8cb1-46ce24de855e
Lokasyon	Computer Science
Tarih	2021-06
Notlar	Bogazici Research Fund ; Japan Science and Technology Agency
Örnek Metin	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

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

Basım Tarihi 2021-06

Basım Yeri - IEEE

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

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2379-8920

Kayıt Numarası f4c8fc0b-8cf7-4048-8cb1-46ce24de855e

Lokasyon Computer Science

Tarih 2021-06

Notlar Bogazici Research Fund ; Japan Science and Technology Agency

Örnek Metin 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