Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: From Demonstrations to Task-Space Specifications: Using Causal Analysis
  to Extract Rule Parameterization from Demonstrations   Learning models of user behaviour is an important problem that is broadly
applicable across many application domains requiring human-robot interaction.
In this work, we show that it is possible to learn generative models for
distinct user behavioural types, extracted from human demonstrations, by
enforcing clustering of preferred task solutions within the latent space. We
use these models to differentiate between user types and to find cases with
overlapping solutions. Moreover, we can alter an initially guessed solution to
satisfy the preferences that constitute a particular user type by
backpropagating through the learned differentiable models. An advantage of
structuring generative models in this way is that we can extract causal
relationships between symbols that might form part of the user's specification
of the task, as manifested in the demonstrations. We further parameterize these
specifications through constraint optimization in order to find a safety
envelope under which motion planning can be performed. We show that the
proposed method is capable of correctly distinguishing between three user
types, who differ in degrees of cautiousness in their motion, while performing
the task of moving objects with a kinesthetically driven robot in a tabletop
environment. Our method successfully identifies the correct type, within the
specified time, in 99% [97.8 - 99.8] of the cases, which outperforms an IRL
baseline. We also show that our proposed method correctly changes a default
trajectory to one satisfying a particular user specification even with unseen
objects. The resulting trajectory is shown to be directly implementable on a
PR2 humanoid robot completing the same task.

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The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
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The categories of this paper are: econ
2
The categories of this paper are: math
17
The categories of this paper are: q-fin