Learning structured task related abstractions
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Date
01/07/2019Author
Penkov, Svetlin Valentinov
Metadata
Abstract
As robots and autonomous agents are to assist people with more tasks in various
domains they need the ability to quickly gain contextual awareness in unseen environments
and learn new tasks. Current state of the art methods rely predominantly
on statistical learning techniques which tend to overfit to sensory signals and often
fail to extract structured task related abstractions. The obtained environment and task
models are typically represented as black box objects that cannot be easily updated or
inspected and provide limited generalisation capabilities.
We address the aforementioned shortcomings of current methods by explicitly
studying the problem of learning structured task related abstractions. In particular, we
are interested in extracting symbolic representations of the environment from sensory
signals and encoding the task to be executed as a computer program. We consider the
standard problem of learning to solve a task by mapping sensory signals to actions
and propose the decomposition of such a mapping into two stages: i) perceiving
symbols from sensory data and ii) using a program to manipulate those symbols in
order to make decisions. This thesis studies the bidirectional interactions between the
agent’s capabilities to perceive symbols and the programs it can execute in order to
solve a task.
In the first part of the thesis we demonstrate that access to a programmatic
description of the task provides a strong inductive bias which facilitates the learning
of structured task related representations of the environment. In order to do so, we first
consider a collaborative human-robot interaction setup and propose a framework for
Grounding and Learning Instances through Demonstration and Eye tracking (GLIDE)
which enables robots to learn symbolic representations of the environment from few
demonstrations. In order to relax the constraints on the task encoding program which
GLIDE assumes, we introduce the perceptor gradients algorithm and prove that it can
be applied with any task encoding program.
In the second part of the thesis we investigate the complement problem of inducing
task encoding programs assuming that a symbolic representations of the
environment is available. Therefore, we propose the p-machine – a novel program
induction framework which combines standard enumerative search techniques with a
stochastic gradient descent optimiser in order to obtain an efficient program synthesiser.
We show that the induction of task encoding programs is applicable to various
problems such as learning physics laws, inspecting neural networks and learning in
human-robot interaction setups.