Role Playing Learning for Socially Concomitant Mobile Robot Navigation

In this paper, we present the Role Playing Learning (RPL) scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks (NN) are constructed to parameterize a stochastic policy that directly maps sensory data collected by the robot to its velocity outputs, while respecting a set of social norms. An efficient simulative learning environment is built with maps and pedestrians trajectories collected from a number of real-world crowd data sets. In each learning iteration, a robot equipped with the NN policy is created virtually in the learning environment to play itself as a companied pedestrian and navigate towards a goal in a socially concomitant manner. Thus, we call this process Role Playing Learning, which is formulated under a reinforcement learning (RL) framework. The NN policy is optimized end-to-end using Trust Region Policy Optimization (TRPO), with consideration of the imperfectness of robot's sensor measurements. Simulative and experimental results are provided to demonstrate the efficacy and superiority of our method

Alternative Approaches to the Empirical Validation of Agent-Based Models

This paper draws on the metaphor of a spectrum of models ranging from the most theory-driven to the most evidence-driven. The issue of concern is the practice and criteria that will be appro- priate to validation of different models. In order to address this concern, two modelling approaches are investigated in some detailed – one from each end of our metaphorical spectrum. Windrum et al. (2007) (http://jasss.soc.surrey.ac.uk/10/2/8.html) claimed strong similarities between agent based social simulation and conventional social science – specifically econometric – approaches to empirical modelling and on that basis considered how econometric validation techniques might be used in empirical social simulations more broadly. An alternative is the approach of the French school of \'companion modelling\' associated with Bousquet, Barreteau, Le Page and others which engages stakeholders in the modelling and validation process. The conventional approach is con- strained by prior theory and the French school approach by evidence. In this sense they are at opposite ends of the theory-evidence spectrum. The problems for validation identified by Windrum et al. are shown to be irrelevant to companion modelling which readily incorporate complexity due to realistically descriptive specifications of individual behaviour and social interaction. The result combines the precision of formal approaches with the richness of narrative scenarios. Companion modelling is therefore found to be practicable and to achieve what is claimed for it and this alone is a key difference from conventional social science including agent based computational economics.Social Simulation, Validation, Companion Modelling, Data Generating Mechanisms, Complexity

What has happened today? Memory visualisation of a robot companion to assist user’s memory

This is the accepted author manuscript version of the following article: "Joan Saez-Pons, Dag SverreSyrdal, and Kerstin Dautenhahn, “What has happened today? Memory visualisation of a robot companion to assist user’s memory”, Journal of Assistive Technologies, Vol. 9 (4): 207-218, 2015." The published version can be found online at: https://doi.org/10.1108/JAT-02-2015-0004 © Emerald Group Publishing Limited 2015 Published by Emerald Group Publishing LimitedPurpose – Memory deterioration is one of the most common cognitive issues associated with ageing. Not being able to remember daily routines (e.g. taking medicine) poses a serious threat to personal independence. Smart homes combined with assistive robots have been suggested as an acceptable solution to support the independent living of the older people. The purpose of this paper is to develop a memory visualisation tool in robots and smart houses following the hypothesis that the use of memory aids will have a positive effect on the cognitive capabilities of older people. Design/methodology/approach – This paper describes the iterative development process and evaluation of a novel interface to visualise the episodic memory of a socially assistive robotic system which could help to improve the memory capabilities of older users. Two experimental studies were carried out to assess usability, usefulness and envisaged use of such a system. Findings – Results show that users find a memory tool for the robot useful to help them remember daily routines and when trying to recall previous events. Usability results emphasise the need to tailor the memory tool to specific age ranges. Originality/value – The research to date provides support that for assistive robots to be a truly useful tool, they must be able to deliver episodic memory visualisation tools that enhance day-to-day living (i.e. environmental information, data on the robot’s actions and human-robot interaction episodes). Equipping a robotic companion with a novel memory visualisation tool for episodic memory is an excellent opportunity to have a robot provide such a functionality (cognitive prosthetics).Peer reviewe

Towards Learning ‘Self’ and Emotional Knowledge in Social and Cultural Human-Agent Interactions

Original article can be found at: http://www.igi-global.com/articles/details.asp?ID=35052 Copyright IGI. Posted by permission of the publisher.This article presents research towards the development of a virtual learning environment (VLE) inhabited by intelligent virtual agents (IVAs) and modeling a scenario of inter-cultural interactions. The ultimate aim of this VLE is to allow users to reflect upon and learn about intercultural communication and collaboration. Rather than predefining the interactions among the virtual agents and scripting the possible interactions afforded by this environment, we pursue a bottomup approach whereby inter-cultural communication emerges from interactions with and among autonomous agents and the user(s). The intelligent virtual agents that are inhabiting this environment are expected to be able to broaden their knowledge about the world and other agents, which may be of different cultural backgrounds, through interactions. This work is part of a collaborative effort within a European research project called eCIRCUS. Specifically, this article focuses on our continuing research concerned with emotional knowledge learning in autobiographic social agents.Peer reviewe