MiRo: An animal-like companion robot with a biomimetic brain-based control system

© 2017 Authors.The MiRo robot is a new pet-sized mobile platform with an emotionally-engaging personality and appearance that has been developed for research on companion robotics and robot-assisted therapy. MiRo has six senses and eight degrees of freedom that are designed to promote human-robot interaction. A distinctive feature is the use of a biomimetic brain-based control system consisting of a layered control architecture alongside centralized mechanisms for integration and action selection. MiRo has been developed by Consequential Robotics, a spin-out of the University of Sheffield, and aims to provide the HRI community with a flexible platform for research and education

Creating a voice for MiRo, the world's first commercial biomimetic robot

Copyright © 2017 ISCA. This paper introduces MiRo-The world's first commercial biomimetic robot-And describes how its vocal system was designed using a real-Time parametric general-purpose mammalian vocal synthesiser tailored to the specific physical characteristics of the robot. MiRo's capabilities will be demonstrated live during the hands-on interactive 'Show & Tell' session at INTERSPEECH-2017

MIRO: A Versatile Biomimetic Edutainment Robot

Here we present MIRO, a companion robot designed to engage users in science and robotics via edutainment. MIRO is a robot that is biomimetic in aesthetics, morphology, behaviour, and control architecture. In this paper, we review how these design choices affect its suitability for a companionship role. In particular, we consider how MIRO's emulation of familiar mammalian body language as one component of a broader biomimetic expressive system provides effective communication of emotional state and intent. We go on to discuss how these features contribute to MIRO's potential in other domains such as healthcare, education, and research

Multiple-model approach to non-linear kernel-based adaptive filtering

Kernel methods now provide standard tools for the solution of function approximation and pattern classification problems. However, it is typically assumed that all data are available for training. More recently, various approaches have been proposed for extending kernel methods to sequential problems whereby the model is updated as each new data point arrives. Whilst these approaches have proven successful in estimating the basic parameters, the problem of estimating the hyperparameters which determine the overall model behaviour, remains essentially unsolved. In this paper a novel approach to the hyperparameters is presented based on a multiple model framework. An ensemble of models with different hyperparameters is trained in parallel, the outputs of which are subsequently combined based on a predictive performance measure. This new approach is sucessfully demonstrated in a standard benchmark time series problem

MiRo: Social Interaction and Cognition in an Animal-like Companion Robot

Future companion and assistive robots will interact directly with end-users in their own homes over extended periods of time. To be useful, and remain engaging over the long-term, these technologies need to pass a new threshold in social robotics-to be aware of people, their identities, emotions and intentions and to adapt their behavior to different individuals. Our immediate goal is to match the social cognition ability of companion animals who recognize people and their intentions without linguistic communication. The MiRo robot is a pet-sized mobile platform, with a brain-based control system and an emotionally-engaging appearance, which is being developed for research on companion robotics, and for applications in education, assistive living and robot-assisted therapy. This paper describes new MiRo capabilities for animal-like perception and social cognition that support the adaptation of behavior towards people and other robots

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

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

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