Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Information-based Visual Robotic Mapping in Unstructured Environments
International Journal of Robotics Research
Whiskerbot: A Robotic Active Touch System Modeled on the Rat Whisker Sensory System
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Surface identification using simple contact dynamics for mobile robots
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A capacitive tactile sensor array for surface texture discrimination
Microelectronic Engineering
A nonparametric Bayesian approach toward robot learning by demonstration
Robotics and Autonomous Systems
Bayesian Nonparametric Inference of Switching Dynamic Linear Models
IEEE Transactions on Signal Processing
Three-dimensional contact imaging with an actuated whisker
IEEE Transactions on Robotics
Humanoid Multimodal Tactile-Sensing Modules
IEEE Transactions on Robotics
Sense of Touch in Robots With Self-Organizing Maps
IEEE Transactions on Robotics
A Simple Tactile Probe for Surface Identification by Mobile Robots
IEEE Transactions on Robotics
Learning Dynamic Tactile Sensing With Robust Vision-Based Training
IEEE Transactions on Robotics
Vibrotactile Recognition and Categorization of Surfaces by a Humanoid Robot
IEEE Transactions on Robotics
Majority Voting: Material Classification by Tactile Sensing Using Surface Texture
IEEE Transactions on Robotics
Tactile Sensing for Mobile Manipulation
IEEE Transactions on Robotics
Methods and Technologies for the Implementation of Large-Scale Robot Tactile Sensors
IEEE Transactions on Robotics
Hi-index | 0.00 |
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.