Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Translating Natural Language Instructions to Computer Programs for Robot
  Manipulation   It is highly desirable for robots that work alongside humans to be able to
understand instructions in natural language. Existing language conditioned
imitation learning models directly predict the actuator commands from the image
observation and the instruction text. Rather than directly predicting actuator
commands, we propose translating the natural language instruction to a Python
function which queries the scene by accessing the output of the object detector
and controls the robot to perform the specified task. This enables the use of
non-differentiable modules such as a constraint solver when computing commands
to the robot. Moreover, the labels in this setup are significantly more
informative computer programs that capture the intent of the expert rather than
teleoperated demonstrations. We show that the proposed method performs better
than training a neural network to directly predict the robot actions.

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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
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The categories of this paper are: hep-lat
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
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The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org