Title
Autonomous tactile perception: A combined improved sensing and Bayesian nonparametric approach
Abstract
In recent years, autonomous robots have increasingly been deployed in unknown environments and required to manipulate or categorize unknown objects. In order to cope with these unfamiliar situations, improvements must be made both in sensing technologies and in the capability to autonomously train perception models. In this paper, we explore this problem in the context of tactile surface identification and categorization. Using a highly-discriminant tactile probe based upon large bandwidth, triple axis accelerometer that is sensitive to surface texture and material properties, we demonstrate that unsupervised learning for surface identification with this tactile probe is feasible. To this end, we derived a Bayesian nonparametric approach based on Pitman-Yor processes to model power-law distributions, an extension of our previous work using Dirichlet processes Dallaire et al. (2011). When tested against a large collection of surfaces and without providing the actual number of surfaces, the tactile probe combined with our proposed approach demonstrated near-perfect recognition in many cases and achieved perfect recognition given the right conditions. We consider that our combined improvements demonstrate the feasibility of effective autonomous tactile perception systems.
Year
DOI
Venue
2014
10.1016/j.robot.2013.11.011
Robotics and Autonomous Systems
Keywords
Field
DocType
Tactile sensing,Surface and texture identification,Bayesian nonparametric methods,Accelerometer,Machine learning
Categorization,Computer vision,Computer science,Accelerometer,Unsupervised learning,Bandwidth (signal processing),Artificial intelligence,Dirichlet distribution,Robot,Perception,Machine learning,Bayesian nonparametrics
Journal
Volume
Issue
ISSN
62
4
0921-8890
Citations 
PageRank 
References 
18
0.74
37
Authors
4
Name
Order
Citations
PageRank
Patrick Dallaire1464.04
Philippe Giguère214521.51
Daniel ímond3180.74
Chaib-draa, Brahim41190113.23