Whisking with robots from rat vibrissae to biomimetic technology for active touch

This article summarizes some of the key features of the rat vibrissal system, including the actively controlled sweeping movements of the vibrissae known as whisking, and reviews the past and ongoing research aimed at replicating some of this functionality in biomimetic robots

The Emergence of Action Sequences from Spatial Attention: Insight from Rodent-Like Robots

Animal behaviour is rich, varied, and smoothly integrated. One plausible model of its generation is that behavioural sub-systems compete to command effectors. In small terrestrial mammals, many behaviours are underpinned by foveation, since important effectors (teeth, tongue) are co-located with foveal sensors (microvibrissae, lips, nose), suggesting a central role for foveal selection and foveation in generating behaviour. This, along with research on primate visual attention, inspires an alternative hypothesis, that integrated behaviour can be understood as sequences of foveations with selection being amongst foveation targets based on their salience. Here, we investigate control architectures for a biomimetic robot equipped with a rodent-like vibrissal tactile sensing system, explicitly comparing a salience map model for action guidance with an earlier model implementing behaviour selection. Both architectures generate life-like action sequences, but in the salience map version higher-level behaviours are an emergent consequence of following a shifting focus of attention

Predictive prey pursuit in a whiskered robot

Highly active small mammals need to capture prey rapidly and with a high success rate if they are to survive. We consider the case of the Etruscan shrew, which hunts prey including crickets almost as large as itself, and relies on its whiskers (vibrissae) to complete a kill. We model this hunting behaviour using a whiskered robot. Shrews strike rapidly and accurately after gathering very limited sensory information; we attempt to match this performance by using model-based simultaneous discrimination and localisation of a ‘prey’ robot (i.e. by using strong priors). We report performance that is comparable, given the spatial and temporal scale differences, to shrew performance in most respects

Perception of simple stimuli using sparse data from a tactile whisker array

We introduce a new multi-element sensory array built from tactile whiskers and modelled on the mammalian whisker sensory system. The new array adds, over previous designs, an actuated degree of freedom corresponding approximately to the mobility of the mystacial pad of the animal. We also report on its performance in a preliminary test of simultaneous identification and localisation of simple stimuli (spheres and a plane). The sensory processing system uses prior knowledge of the set of possible stimuli to generate percepts of the form and location of extensive stimuli from sparse and highly localised sensory data. Our results suggest that the additional degree of freedom has the potential to offer a benefit to perception accuracy for this type of sensor. © 2013 Springer-Verlag Berlin Heidelberg

Naive Bayes novelty detection for a moving robot with whiskers

Novelty detection would be a useful ability for any autonomous robot that seeks to categorize a new environment or notice unexpected changes in its present one. A biomimetic robot (SCRATCHbot) inspired by the rat whisker system was here used to examine the performance of a novelty detection algorithm based on a 'naive' implementation of Bayes rule. Naive Bayes algorithms are known to be both efficient and effective, and also have links with proposed neural mechanisms for decision making. To examine novelty detection, the robot first used its whiskers to sense an empty floor, after which it was tested with a textured strip placed in its path. Given only its experience of the familiar situation, the robot was able to distinguish the novel event and localize it in time. Performance increased with the number of whiskers, indicating benefits from integrating over multiple streams of information. Considering the generality of the algorithm, we suggest that such novelty detection could have widespread applicability as a trigger to react to important features in the robot's environment. © 2010 IEEE

The robot vibrissal system: Understanding mammalian sensorimotor co-ordination through biomimetics

Chapter 10 The Robot Vibrissal System: Understanding Mammalian Sensorimotor Co-ordination Through Biomimetics Tony J. Prescott, Ben Mitchinson, Nathan F. Lepora, Stuart P. Wilson, Sean R. Anderson, John Porrill, Paul Dean, Charles ..

Towards hierarchical blackboard mapping on a whiskered robot

The paradigm case for robotic mapping assumes large quantities of sensory information which allow the use of relatively weak priors. In contrast, the present study considers the mapping problem for a mobile robot, CrunchBot, where only sparse, local tactile information from whisker sensors is available. To compensate for such weak likelihood information, we make use of low-level signal processing and strong hierarchical object priors. Hierarchical models were popular in classical blackboard systems but are here applied in a Bayesian setting as a mapping algorithm. The hierarchical models require reports of whisker distance to contact and of surface orientation at contact, and we demonstrate that this information can be retrieved by classifiers from strain data collected by CrunchBot's physical whiskers. We then provide a demonstration in simulation of how this information can be used to build maps (but not yet full SLAM) in an zero-odometry-noise environment containing walls and table-like hierarchical objects. © 2012 Elsevier B.V. All rights reserved