Abel: Integrating Humanoid Body, Emotions, and Time Perception to Investigate Social Interaction and Human Cognition

Humanoids have been created for assisting or replacing humans in many applications, providing encouraging results in contexts where social and emotional interaction is required, such as healthcare, education, and therapy. Bioinspiration, that has often guided the design of their bodies and minds, made them also become excellent research tools, probably the best platform by which we can model, test, and understand the human mind and behavior. Driven by the aim of creating a believable robot for interactive applications, as well as a research platform for investigating human cognition and emotion, we are constructing a new humanoid social robot: Abel. In this paper, we discussed three of the fundamental principles that motivated the design of Abel and its cognitive and emotional system: hyper-realistic humanoid aesthetics, human-inspired emotion processing, and human-like perception of time. After reporting a brief state-of-the-art on the related topics, we present the robot at its stage of development, what are the perspectives for its application, and how it could satisfy the expectations as a tool to investigate the human mind, behavior, and consciousness

Angle and position perception for exploration with active touch

Over the past few decades the design of robots has gradually improved, allowing them to perform complex tasks in interaction with the world. To behave appropriately, robots need to make perceptual decisions about their environment using their various sensory modalities. Even though robots are being equipped with progressively more accurate and advanced sensors, dealing with uncertainties from the world and their sensory processes remains an unavoidable necessity for autonomous robotics. The challenge is to develop robust methods that allow robots to perceive their environment while managing uncertainty and optimizing their decision making. These methods can be inspired by the way humans and animals actively direct their senses towards locations for reducing uncertainties from perception [1]. For instance, humans not only use their hands and fingers for exploration and feature extraction but also their movements are guided according to what it is being perceived [2]. This behaviour is also present in the animal kingdom, such as rats that actively explore the environment by appropriately moving their whiskers [3]. © 2013 Springer-Verlag Berlin Heidelberg

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

An investigation into spike-based neuromorphic approaches for artificial olfactory systems

The implementation of neuromorphic methods has delivered promising results for vision and auditory sensors. These methods focus on mimicking the neuro-biological architecture to generate and process spike-based information with minimal power consumption. With increasing interest in developing low-power and robust chemical sensors, the application of neuromorphic engineering concepts for electronic noses has provided an impetus for research focusing on improving these instruments. While conventional e-noses apply computationally expensive and power-consuming data-processing strategies, neuromorphic olfactory sensors implement the biological olfaction principles found in humans and insects to simplify the handling of multivariate sensory data by generating and processing spike-based information. Over the last decade, research on neuromorphic olfaction has established the capability of these sensors to tackle problems that plague the current e-nose implementations such as drift, response time, portability, power consumption and size. This article brings together the key contributions in neuromorphic olfaction and identifies future research directions to develop near-real-time olfactory sensors that can be implemented for a range of applications such as biosecurity and environmental monitoring. Furthermore, we aim to expose the computational parallels between neuromorphic olfaction and gustation for future research focusing on the correlation of these senses

Sensory augmentation with distal touch: The tactile helmet project

The Tactile Helmet is designed to augment a wearer's senses with a long range sense of touch. Tactile specialist animals such as rats and mice are capable of rapidly acquiring detailed information about their environment from their whiskers by using task-sensitive strategies. Providing similar information about the nearby environment, in tactile form, to a human operator could prove invaluable for search and rescue operations, or for partially-sighted people. Two key aspects of the Tactile Helmet are sensory augmentation, and active sensing. A haptic display is used to provide the user with ultrasonic range information. This can be interpreted in addition to, rather than instead of, visual or auditory information. Active sensing systems "are purposive and information-seeking sensory systems, involving task specific control of the sensory apparatus" [1]. The integration of an accelerometer allows the device to actively gate the delivery of sensory information to the user, depending on their movement. Here we describe the hardware, sensory transduction and characterisation of the Tactile Helmet device, before outlining potential use cases and benefits of the system. © 2013 Springer-Verlag Berlin Heidelberg

Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification

Existing methods in neuromorphic olfaction mainly focus on implementing the data transformation based on the neurobiological architecture of the olfactory pathway. While the transformation is pivotal for the sparse spike-based representation of odor data, classification techniques based on the bio-computations of the higher brain areas, which process the spiking data for identification of odor, remain largely unexplored. This paper argues that brain-inspired spiking neural networks constitute a promising approach for the next generation of machine intelligence for odor data processing. Inspired by principles of brain information processing, here we propose the first spiking neural network method and associated deep machine learning system for classification of odor data. The paper demonstrates that the proposed approach has several advantages when compared to the current state-of-the-art methods. Based on results obtained using a benchmark dataset, the model achieved a high classification accuracy for a large number of odors and has the capacity for incremental learning on new data. The paper explores different spike encoding algorithms and finds that the most suitable for the task is the step-wise encoding function. Further directions in the brain-inspired study of odor machine classification include investigation of more biologically plausible algorithms for mapping, learning, and interpretation of odor data along with the realization of these algorithms on some highly parallel and low power consuming neuromorphic hardware devices for real-world applications