Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Manifold Particle Filter for State Estimation on High-dimensional
  Implicit Manifolds   We estimate the state a noisy robot arm and underactuated hand using an
Implicit Manifold Particle Filter (MPF) informed by touch sensors. As the robot
touches the world, its state space collapses to a contact manifold that we
represent implicitly using a signed distance field. This allows us to extend
the MPF to higher (six or more) dimensional state spaces. Earlier work (which
explicitly represents the contact manifold) only shows the MPF in two or three
dimensions. Through a series of experiments, we show that the implicit MPF
converges faster and is more accurate than a conventional particle filter
during periods of persistent contact. We present three methods of sampling the
implicit contact manifold, and compare them in experiments.

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The categories of this paper are: nlin
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: eess
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ph
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The categories of this paper are: physics
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: mtrl-th