Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Semantic Embedding Spaces for Slicing Vegetables   In this work, we present an interaction-based approach to learn semantically
rich representations for the task of slicing vegetables. Unlike previous
approaches, we focus on object-centric representations and use auxiliary tasks
to learn rich representations using a two-step process. First, we use simple
auxiliary tasks, such as predicting the thickness of a cut slice, to learn an
embedding space which captures object properties that are important for the
task of slicing vegetables. In the second step, we use these learned latent
embeddings to learn a forward model. Learning a forward model affords us to
plan online in the latent embedding space and forces our model to improve its
representations while performing the slicing task. To show the efficacy of our
approach we perform experiments on two different vegetables: cucumbers and
tomatoes. Our experimental evaluation shows that our method is able to capture
important semantic properties for the slicing task, such as the thickness of
the vegetable being cut. We further show that by using our learned forward
model, we can plan for the task of vegetable slicing.

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The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
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The categories of this paper are: math
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The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess