Intrinsic Robotic Introspection: Learning Internal States From Neuron Activations

Pitsillos, N., Pore, A., Jensen, B. S. and Aragon Camarasa, G. (2021) Intrinsic Robotic Introspection: Learning Internal States From Neuron Activations. In: IEEE International Conference on Development and Learning (ICDL 2021), Beijing, China, 23-26 Aug 2021, ISBN 9781728162423 (doi: 10.1109/ICDL49984.2021.9515672)

Abstract

We present an introspective framework inspired by the process of how humans perform introspection. Our working assumption is that neural network activations encode information, and building internal states from these activations can improve the performance of an actor-critic model. We perform experiments where we first train a Variational Autoencoder model to reconstruct the activations of a feature extraction network and use the latent space to improve the performance of an actor-critic when deciding which low-level robotic behaviour to execute. We show that internal states reduce the number of episodes needed by about 1300 episodes while training an actor-critic, denoting faster convergence to get a high success value while completing a robotic task.

• Item Type: Conference Proceedings
• Additional Information: This research has been supported by EPSRC DTA No. 2279292 and NVIDIA Corporation for the donation of the Titan Xp GPU.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Aragon Camarasa, Dr Gerardo and Pitsillos, Nikos and Jensen, Dr Bjorn
• Authors: Pitsillos, N., Pore, A., Jensen, B. S., and Aragon Camarasa, G.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Computing Science
• ISBN: 9781728162423
• Published Online: 20 August 2021
• Copyright Holders: Copyright Copyright © 2021, IEEE
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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