Towards self-attention based visual navigation in the real world
- URL: http://arxiv.org/abs/2209.07043v2
- Date: Mon, 19 Sep 2022 08:56:04 GMT
- Title: Towards self-attention based visual navigation in the real world
- Authors: Jaime Ruiz-Serra, Jack White, Stephen Petrie, Tatiana Kameneva, Chris
McCarthy
- Abstract summary: Vision guided navigation requires processing complex visual information to inform task-orientated decisions.
Deep Reinforcement Learning agents trained in simulation often exhibit unsatisfactory results when deployed in the real-world.
This is the first demonstration of a self-attention based agent successfully trained in navigating a 3D action space, using less than 4000 parameters.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Vision guided navigation requires processing complex visual information to
inform task-orientated decisions. Applications include autonomous robots,
self-driving cars, and assistive vision for humans. A key element is the
extraction and selection of relevant features in pixel space upon which to base
action choices, for which Machine Learning techniques are well suited. However,
Deep Reinforcement Learning agents trained in simulation often exhibit
unsatisfactory results when deployed in the real-world due to perceptual
differences known as the $\textit{reality gap}$. An approach that is yet to be
explored to bridge this gap is self-attention. In this paper we (1) perform a
systematic exploration of the hyperparameter space for self-attention based
navigation of 3D environments and qualitatively appraise behaviour observed
from different hyperparameter sets, including their ability to generalise; (2)
present strategies to improve the agents' generalisation abilities and
navigation behaviour; and (3) show how models trained in simulation are capable
of processing real world images meaningfully in real time. To our knowledge,
this is the first demonstration of a self-attention based agent successfully
trained in navigating a 3D action space, using less than 4000 parameters.
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