Robotic Action-state Evaluation via Siamese Neural Network

Allbwn ymchwil: Cyfraniad at gynhadleddPapuradolygiad gan gymheiriaid

17 Wedi eu Llwytho i Lawr (Pure)


Robotic imitation learning methods assist robots to operate in evolving and unconstrained environments. However, current robotic state representation imitation learning methods still must involve human experts to provide sparse rewards that indicate whether robots successfully complete tasks. However, enabling robots to make the action-state evaluation autonomously still remains a challenge, especially for multi-stage complex tasks. Therefore, in this work, we propose a novel Siamese neural network-based robotic action state evaluation system in an imitation learning system, so as to replace human experts in a multi-stage imitation learning process and improve the learning efficiency. One target learning footage is divided into several stages; for each stage, two Siamese network frameworks are created to assess the robotic action-states in terms of both movement and environment changes.
Iaith wreiddiolSaesneg
StatwsCyhoeddwyd - 2022
DigwyddiadRobotics for Unconstrained Environments: Robotics for Unconstrained Environments - Aberystwyth, Aberystwyth, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon
Hyd: 25 Awst 202226 Awst 2022
Rhif y gynhadledd: 5


CynhadleddRobotics for Unconstrained Environments
Teitl crynoUK-RAS 2022
Gwlad/TiriogaethTeyrnas Unedig Prydain Fawr a Gogledd Iwerddon
Cyfnod25 Awst 202226 Awst 2022
ArallThe UKRAS conference series has an impor-
tant aim of inclusion to allow young researchers
to present their work and share ideas with peers
and more senior researchers. As such, this year’s
conference attempted to offer as many opportunities
for discussions.
The conference also provided a platform to high-
light UK robotics research in the area of uncon-
strained environments.
Cyfeiriad rhyngrwyd

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