Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Periodic Tasks from Human Demonstrations   We develop a method for learning periodic tasks from visual demonstrations.
The core idea is to leverage periodicity in the policy structure to model
periodic aspects of the tasks. We use active learning to optimize parameters of
rhythmic dynamic movement primitives (rDMPs) and propose an objective to
maximize the similarity between the motion of objects manipulated by the robot
and the desired motion in human video demonstrations. We consider tasks with
deformable objects and granular matter whose state is challenging to represent
and track: wiping surfaces with a cloth, winding cables/wires, stirring
granular matter with a spoon. Our method does not require tracking markers or
manual annotations. The initial training data consists of 10-minute videos of
random unpaired interactions with objects by the robot and human. We use these
for unsupervised learning of a keypoint model to get task-agnostic visual
correspondences. Then, we use Bayesian optimization to optimize rDMPs from a
single human video demonstration within few robot trials. We present simulation
and hardware experiments to validate our approach.

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The categories of this paper are: cmp-lg
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
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The categories of this paper are: plasm-ph