Disentangled Relational Representations for Explaining and Learning from
Demonstration
release_r5lddkk2gzd5fovhapi5s2hdri
by
Yordan Hristov, Daniel Angelov, Michael Burke, Alex Lascarides,
Subramanian Ramamoorthy
2019
Abstract
Learning from demonstration is an effective method for human users to
instruct desired robot behaviour. However, for most non-trivial tasks of
practical interest, efficient learning from demonstration depends crucially on
inductive bias in the chosen structure for rewards/costs and policies. We
address the case where this inductive bias comes from an exchange with a human
user. We propose a method in which a learning agent utilizes the information
bottleneck layer of a high-parameter variational neural model, with auxiliary
loss terms, in order to ground abstract concepts such as spatial relations. The
concepts are referred to in natural language instructions and are manifested in
the high-dimensional sensory input stream the agent receives from the world. We
evaluate the properties of the latent space of the learned model in a
photorealistic synthetic environment and particularly focus on examining its
usability for downstream tasks. Additionally, through a series of controlled
table-top manipulation experiments, we demonstrate that the learned manifold
can be used to ground demonstrations as symbolic plans, which can then be
executed on a PR2 robot.
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