Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Jacobian-Based Inverse Kinematics with Sim-to-Real Transfer of
  Soft Robots by Learning   This paper presents an efficient learning-based method to solve the inverse
kinematic (IK) problem on soft robots with highly non-linear deformation. The
major challenge of efficiently computing IK for such robots is due to the lack
of analytical formulation for either forward or inverse kinematics. To address
this challenge, we employ neural networks to learn both the mapping function of
forward kinematics and also the Jacobian of this function. As a result,
Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real
training transfer strategy is conducted to make this approach more practical.
We first generate a large number of samples in a simulation environment for
learning both the kinematic and the Jacobian networks of a soft robot design.
Thereafter, a sim-to-real layer of differentiable neurons is employed to map
the results of simulation to the physical hardware, where this sim-to-real
layer can be learned from a very limited number of training samples generated
on the hardware. The effectiveness of our approach has been verified on
pneumatic-driven soft robots for path following and interactive positioning.

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The categories of this paper are: eess
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The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
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The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: ao-sci
3
The categories of this paper are: physics
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The categories of this paper are: supr-con
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The categories of this paper are: q-fin
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The categories of this paper are: q-alg
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The categories of this paper are: math
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The categories of this paper are: hep-lat
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The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org