Evolution of central pattern generators for the control of a five-link bipedal walking mechanism

Central pattern generators (CPGs), with a basis is neurophysiological studies, are a type of neural network for the generation of rhythmic motion. While CPGs are being increasingly used in robot control, most applications are hand-tuned for a specific task and it is acknowledged in the field that generic methods and design principles for creating individual networks for a given task are lacking. This study presents an approach where the connectivity and oscillatory parameters of a CPG network are determined by an evolutionary algorithm with fitness evaluations in a realistic simulation with accurate physics. We apply this technique to a five-link planar walking mechanism to demonstrate its feasibility and performance. In addition, to see whether results from simulation can be acceptably transferred to real robot hardware, the best evolved CPG network is also tested on a real mechanism. Our results also confirm that the biologically inspired CPG model is well suited for legged locomotion, since a diverse manifestation of networks have been observed to succeed in fitness simulations during evolution.Comment: 11 pages, 9 figures; substantial revision of content, organization, and quantitative result

Active Choice of Teachers, Learning Strategies and Goals for a Socially Guided Intrinsic Motivation Learner

International audienceWe present an active learning architecture that allows a robot to actively learn which data collection strategy is most efficient for acquiring motor skills to achieve multiple outcomes, and generalise over its experience to achieve new outcomes. The robot explores its environment both via interactive learning and goal-babbling. It learns at the same time when, who and what to actively imitate from several available teachers, and learns when not to use social guidance but use active goal-oriented self-exploration. This is formalised in the framework of life-long strategic learning. The proposed architecture, called Socially Guided Intrinsic Motivation with Active Choice of Teacher and Strategy (SGIM-ACTS), relies on hierarchical active decisions of what and how to learn driven by empirical evaluation of learning progress for each learning strategy. We illustrate with an experiment where a simulated robot learns to control its arm for realising two kinds of different outcomes. It has to choose actively and hierarchically at each learning episode: 1) what to learn: which outcome is most interesting to select as a goal to focus on for goal-directed exploration; 2) how to learn: which data collection strategy to use among self-exploration, mimicry and emulation; 3) once he has decided when and what to imitate by choosing mimicry or emulation, then he has to choose who to imitate, from a set of different teachers. We show that SGIM-ACTS learns significantly more efficiently than using single learning strategies, and coherently selects the best strategy with respect to the chosen outcome, taking advantage of the available teachers (with different levels of skills)

Towards a full spectrum diagnosis of autistic behaviours using human robot interactions

Autism Spectrum Disorder (ASD) is conceptualised by the Diag-nostic and Statistical Manual of Mental Disorders (DSM-V) [1] asa spectrum, and diagnosis involves scoring behaviours in termsof a severity scale. Whilst the application of automated systemsand socially interactive robots to ASD diagnosis would increase ob-jectivity and standardisation, most of the existing systems classifybehaviours in a binary fashion (ASD vs. non-ASD). To be useful ininterventions, and to overcome ethical concerns regarding overlysimplied diagnostic measures, a robot therefore needs to be ableto classify target behaviours along a continuum, rather than indiscrete groups. Here we discuss an approach toward this goalwhich has the potential to identify the full spectrum of observableASD traits

Healthcare Robotics

Robots have the potential to be a game changer in healthcare: improving health and well-being, filling care gaps, supporting care givers, and aiding health care workers. However, before robots are able to be widely deployed, it is crucial that both the research and industrial communities work together to establish a strong evidence-base for healthcare robotics, and surmount likely adoption barriers. This article presents a broad contextualization of robots in healthcare by identifying key stakeholders, care settings, and tasks; reviewing recent advances in healthcare robotics; and outlining major challenges and opportunities to their adoption.Comment: 8 pages, Communications of the ACM, 201

Planning Based System for Child-Robot Interaction in Dynamic Play Environments

This paper describes the initial steps towards the design of a robotic system that intends to perform actions autonomously in a naturalistic play environment. At the same time it aims for social human-robot interaction~(HRI), focusing on children. We draw on existing theories of child development and on dimensional models of emotions to explore the design of a dynamic interaction framework for natural child-robot interaction. In this dynamic setting, the social HRI is defined by the ability of the system to take into consideration the socio-emotional state of the user and to plan appropriately by selecting appropriate strategies for execution. The robot needs a temporal planning system, which combines features of task-oriented actions and principles of social human robot interaction. We present initial results of an empirical study for the evaluation of the proposed framework in the context of a collaborative sorting game