Conference Paper

Sensing with Artificial Tactile Sensors: An Investigation of Spatio-temporal Inference.

DOI: 10.1007/978-3-642-23232-9_23 Conference: Towards Autonomous Robotic Systems - 12th Annual Conference, TAROS 2011, Sheffield, UK, August 31 - September 2, 2011. Proceedings
Source: DBLP

ABSTRACT The ease and efficiency with which biological systems deal with several real world problems, that have been persistently challenging
to implement in artificial systems, is a key motivation in biomimetic robotics. In interacting with its environment, the first
challenge any agent faces is to extract meaningful patterns in the inputs from its sensors. This problem of pattern recognition
has been characterized as an inference problem in cortical computation. The work presented here implements the hierarchical
temporal memory (HTM) model of cortical computation using inputs from an array of artificial tactile sensors to recognize
simple Braille patterns. Although the current work has been implemented using a small array of robot whiskers, the architecture
can be extended to larger arrays of sensors of any arbitrary modality.

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    ABSTRACT: Good performance in unstructured/uncertain environments is an ongoing problem in robotics; in biology, it is an everyday observation. Here, we model a particular biological system—hunting in the Etruscan shrew—as a case study in biomimetic robot design. These shrews strike rapidly and accurately after gathering very limited sensory information from their whiskers; we attempt to mimic this performance by using model-based simultaneous discrimination and localisation of a ‘prey’ robot (i.e. by using strong priors to make sense of limited sensory data), building on our existing low-level models of attention and appetitive behaviour in small mammals. We report performance that is comparable, given the spatial and temporal scale differences, to shrew performance, and discuss what this study reveals about biomimetic robot design in general.
    Robotics and Autonomous Systems 03/2014; 62(3):366–375. DOI:10.1016/j.robot.2013.08.013 · 1.11 Impact Factor