Friction Variability in Planar Pushing Data: Anisotropic Friction and Data-Collection Bias
Name
1802.10089.pdf
Size
4.59 MB
Format
Adobe PDF
Checksum (MD5)
575efd296ea01edec9d39a0691cad663
Author(s)
Ma, Daolin
Rodriguez Garcia, Alberto
Date Issued
June 2018
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Ma, Daolin, and Alberto Rodriguez. “Friction Variability in Planar Pushing Data: Anisotropic Friction and Data-Collection Bias.” IEEE Robotics and Automation Letters 3, no. 4 (October 2018): 3232–3239.
Version
Author's final manuscript
Abstract
Friction plays a key role in manipulating objects. Most of what we do with
our hands, and most of what robots do with their grippers, is based on the
ability to control frictional forces. This paper aims to better understand the
variability and predictability of planar friction. In particular, we focus on
the analysis of a recent dataset on planar pushing by Yu et al. [1] devised to
create a data-driven footprint of planar friction.
We show in this paper how we can explain a significant fraction of the
observed unconventional phenomena, e.g., stochasticity and multi-modality, by
combining the effects of material non-homogeneity, anisotropy of friction and
biases due to data collection dynamics, hinting that the variability is
explainable but inevitable in practice.
We introduce an anisotropic friction model and conduct simulation experiments
comparing with more standard isotropic friction models. The anisotropic
friction between object and supporting surface results in convergence of
initial condition during the automated data collection. Numerical results
confirm that the anisotropic friction model explains the bias in the dataset
and the apparent stochasticity in the outcome of a push. The fact that the data
collection process itself can originate biases in the collected datasets,
resulting in deterioration of trained models, calls attention to the data
collection dynamics.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/LRA.2018.2851026
