Context based echo state networks for robot movement primitives

Name: Context based echo state networks for robot movement primitives
Author: Amirshirzad, Negin, Asada, M., Öztop, Erhan

İsim	Context based echo state networks for robot movement primitives
Yazar	Amirshirzad, Negin, Asada, M., Öztop, Erhan
Basım Tarihi:	2023
Basım Yeri	- IEEE
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	979-8-3503-3670-2
Kayıt Numarası	09c604c9-723d-4fc3-ab27-58a3dcb54d7a
Lokasyon	Computer Science
Tarih	2023
Notlar	New Energy and Industrial Technology Development Organization (NEDO) ; Japan Science & Technology Agency (JST) ; Core Research for Evolutional Science and Technology (CREST)
Örnek Metin	Reservoir Computing, in particular Echo State Networks (ESNs) offer a lightweight solution for time series representation and prediction. An ESN is based on a discrete time random dynamical system that is used to output a desired time series with the application of a learned linear readout weight vector. The simplicity of the learning suggests that an ESN can be used as a lightweight alternative for movement primitive representation in robotics. In this study, we explore this possibility and develop Context-based Echo State Networks (CESNs), and demonstrate their applicability to robot movement generation. The CESNs are designed for generating joint or Cartesian trajectories based on a user definable context input. The context modulates the dynamics represented by the ESN involved. The linear read-out weights then can pick up the context-dependent dynamics for generating different movement patterns for different contexts. To achieve robust movement execution and generalization over unseen contexts, we introduce a novel data augmentation mechanism for ESN training. We show the effectiveness of our approach in a learning from demonstration setting. To be concrete, we teach the robot reaching and obstacle avoidance tasks in simulation and in real-world, which shows that the developed system, CESN provides a lightweight movement primitive representation system that facilitate robust task execution with generalization ability for unseen seen contexts, including extrapolated ones.

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Özyeğin Üniversitesi

Kaynağa git

Context based echo state networks for robot movement primitives

Yazar Amirshirzad, Negin, Asada, M., Öztop, Erhan

Basım Tarihi 2023

Basım Yeri - IEEE

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 979-8-3503-3670-2

Kayıt Numarası 09c604c9-723d-4fc3-ab27-58a3dcb54d7a

Lokasyon Computer Science

Tarih 2023

Notlar New Energy and Industrial Technology Development Organization (NEDO) ; Japan Science & Technology Agency (JST) ; Core Research for Evolutional Science and Technology (CREST)

Örnek Metin Reservoir Computing, in particular Echo State Networks (ESNs) offer a lightweight solution for time series representation and prediction. An ESN is based on a discrete time random dynamical system that is used to output a desired time series with the application of a learned linear readout weight vector. The simplicity of the learning suggests that an ESN can be used as a lightweight alternative for movement primitive representation in robotics. In this study, we explore this possibility and develop Context-based Echo State Networks (CESNs), and demonstrate their applicability to robot movement generation. The CESNs are designed for generating joint or Cartesian trajectories based on a user definable context input. The context modulates the dynamics represented by the ESN involved. The linear read-out weights then can pick up the context-dependent dynamics for generating different movement patterns for different contexts. To achieve robust movement execution and generalization over unseen contexts, we introduce a novel data augmentation mechanism for ESN training. We show the effectiveness of our approach in a learning from demonstration setting. To be concrete, we teach the robot reaching and obstacle avoidance tasks in simulation and in real-world, which shows that the developed system, CESN provides a lightweight movement primitive representation system that facilitate robust task execution with generalization ability for unseen seen contexts, including extrapolated ones.