Decision-making is often accompanied by a degree of confidence on whether a choice is correct. Decision uncertainty, or lack in confidence, may lead to change-of-mind. Studies have identified the behavioural characteristics associated with decision confidence or change-of-mind, and their neural correlates. Although several theoretical accounts have been proposed, there is no neural model that can compute decision uncertainty and explain its effects on change-of-mind. We propose a neuronal circuit model that computes decision uncertainty while accounting for a variety of behavioural and neural data of decision confidence and change-of-mind, including testable model predictions. Our theoretical analysis suggests that change-of-mind occurs due to the presence of a transient uncertainty-induced choice-neutral stable steady state and noisy fluctuation within the neuronal network. Our distributed network model indicates that the neural basis of change-of-mind is more distinctively identified in motor-based neurons. Overall, our model provides a framework that unifies decision confidence and change-of-mind.

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Taxis is a well-known steering technique used by simple animals to approach a stimulus in the real world. However, no mathematical motion model of taxis can be found in the literature. This article derives the differential ordinary equations describing the motion of a Braitenberg vehicle, a widely used taxis model. A qualitative technique for non-linear dynamical systems analysis is applied to investigate the motion generated by the model. Validation of the analysis is performed through several simulations, and conditions for the stimulus source to be reached are obtained. This work fills the theoretical hole in formal models of Braitenberg vehicles and thereby provides theoretical support for the many previous experimental uses of those vehicles for steering tasks.

Abstract:Learning skills or knowledge online from experiences is attractive for robots because it permits them to develop new behavior autonomously. However, the onus lies with the system designer to specify which skills or knowledge the robot should learn. Experience-based goal generation algorithms permit a robot to decide autonomously what it will to learn. This paper presents an adaptive resonance theory approach to experience-based generation of approach, avoidance, maintenance and achievement goals for a mobile robot. An experimental analysis is conducted to explore the relationship between algorithm parameters and goals generated on a simulated ePuck robot. Results show how parameter choice influences the number, stability and nature of generated goals. We identify the weight representations, distance functions and update rules that are appropriate for a mobile robot to generate maintenance and achievement goals.

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Braitenberg vehicles have been used experimentally for decades in robotics with limited empirical understanding. This paper presents the first mathematical model of the vehicle 2b, displaying so-called aggression behaviour, and analyses the possible trajectories for point-like smooth stimulus sources. This sensory-motor steering control mechanism is used to implement biologically grounded target approach, target-seeking or obstacle-avoidance behaviour. However, the analysis of the resulting model reveals that complex and unexpected trajectories can result even for point-like stimuli. We also prove how the implementation of the controller and the vehicle morphology interact to affect the behaviour of the vehicle. This work provides a better understanding of Braitenberg vehicle 2b, explains experimental results and paves the way for a formally grounded application on robotics as well as for a new way of understanding target seeking in biology.

Joint manipulation and object exchange are common in many everyday scenarios. Although they are trivial tasks for humans, they are still very challenging for robots. Existing approaches for robot-tohuman object handover assume that there is no fault during the transfer. However, unintentional perturbation forces can be occasionally applied to the object, resulting in the robot and the object being damaged, for example by being dropped. In this paper we present a novel approach to handover objects in a reliable manner while ensuring the safety of the robot and the object. Relying on tactile sensing, the system uses an effort controller to adapt the grasp forces in the presence of perturbations. Moreover, the proposed approach identifies a perturbation being applied on the object. When a perturbation event is detected, the algorithm classifies the direction of the pulling forces to decide whether to release it or not. The reliable handover system was implemented using a Shadow Robot hand equipped with BioTAC tactile sensors. Our results show that the system correctly adapts to the forces applied on the object to maintain the grasp and only releases the object if the human receiver pulls in the right direction.

Because of their apparent simplicity, Braitenberg vehicles have been extensively used in robotics on an empirical basis. However, the lack of a backing up formal theory turns their application into an educated guess of parameter tunning. This paper provides a mathematical model of Braitenberg vehicles 2 and 3 as non-linear dynamical systems, which serves as a theoretical ground to fully exploit them for robotic applications and to create animated agents in artificial life or computer games. The behaviour of the vehicles is analysed using theory of dynamical systems under general conditions, and hints on how to generate desired behaviours are given. Results show that vehicles 2 and 3 can be used to implement bio-inspired navigation like; target reaching and stimulus avoidance, which constitute a set of navigation primitives or basis for navigation behaviour. Through a new theoretical approach, his work paves the way to a proper understanding of Braitenberg vehicles and to an extension of their applicability.