Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Object Inertial Parameter Identification for Collaborative Robots   Collaborative robots (cobots) are machines designed to work safely alongside
people in human-centric environments. Providing cobots with the ability to
quickly infer the inertial parameters of manipulated objects will improve their
flexibility and enable greater usage in manufacturing and other areas. To
ensure safety, cobots are subject to kinematic limits that result in low
signal-to-noise ratios (SNR) for velocity, acceleration, and force-torque data.
This renders existing inertial parameter identification algorithms
prohibitively slow and inaccurate. Motivated by the desire for faster model
acquisition, we investigate the use of an approximation of rigid body dynamics
to improve the SNR. Additionally, we introduce a mass discretization method
that can make use of shape information to quickly identify plausible inertial
parameters for a manipulated object. We present extensive simulation studies
and real-world experiments demonstrating that our approach complements existing
inertial parameter identification methods by specifically targeting the typical
cobot operating regime.

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The categories of this paper are: solv-int
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The categories of this paper are: stat
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The categories of this paper are: dg-ga
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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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: gr-qc
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The categories of this paper are: ao-sci
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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess
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The categories of this paper are: q-alg
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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas