Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Vision-Based Multi-Task Manipulation for Inexpensive Robots Using
  End-To-End Learning from Demonstration   We propose a technique for multi-task learning from demonstration that trains
the controller of a low-cost robotic arm to accomplish several complex picking
and placing tasks, as well as non-prehensile manipulation. The controller is a
recurrent neural network using raw images as input and generating robot arm
trajectories, with the parameters shared across the tasks. The controller also
combines VAE-GAN-based reconstruction with autoregressive multimodal action
prediction. Our results demonstrate that it is possible to learn complex
manipulation tasks, such as picking up a towel, wiping an object, and
depositing the towel to its previous position, entirely from raw images with
direct behavior cloning. We show that weight sharing and reconstruction-based
regularization substantially improve generalization and robustness, and
training on multiple tasks simultaneously increases the success rate on all
tasks.

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The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
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The categories of this paper are: solv-int
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The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom