Temporal Patterns in Multi-modal Social Interaction between Elderly Users and Service Robot

Social interaction, especially for older people living alone is a challenge currently facing human-robot interaction (HRI). User interfaces to manage service robots in home environments need to be tailored for older people. Multi-modal interfaces providing users with more than one communication option seem promising. There has been little research on user preference towards HRI interfaces; most studies have focused on utility and functionality of the interface. In this paper, we took both objective observations and participants’ opinions into account in studying older users with a robot partner. Our study was under the framework of the EU FP7 Robot-Era Project. The developed dual-modal robot interface offered older users options of speech or touch screen to perform tasks. Fifteen people aged from 70 to 89 years old, participated. We analyzed the spontaneous actions of the participants, including their attentional activities (eye contacts) and conversational activities, the temporal characteristics (timestamps, duration of events, event transitions) of these social behaviours, as well as questionnaires. This combination of data distinguishes it from other studies that focused on questionnaire ratings only. There were three main findings. First, the design of the Robot-Era interface was very acceptable for older users. Secondly, most older people used both speech and tablet to perform the food delivery service, with no difference in their preferences towards either. Thirdly, these older people had frequent and long-duration eye contact with the robot during their conversations, showing patience when expecting the robot to respond. They enjoyed the service. Overall, social engagement with the robot demonstrated by older people was no different from what might be expected towards a human partner. This study is an early attempt to reveal the social connections between human beings and a personal robot in real life. Our observations and findings should inspire new insights in HRI research and eventually contribute to next-generation intelligent robot developmen

Generation of rapidly-exploring random trees by using a new class of membrane systems

Methods based on Rapidly-exploring Random Trees (RRTs) have been in use in robotics to solve motion planning problems for nearly two decades. On the other hand, models based on Enzymatic Numerical P systems (ENPS) have been applied to robot controllers for more than six years. These controllers in real robots handle the power of motors ac- cording to motion commands usually generated by planning algorithms, but today there is a lack of planning algorithms based on membrane sys- tems for robotics. With this motivation, we provide in this paper a new variant of ENPS called Random Enzymatic Numerical P systems with Proteins and Shared Memory (RENPSM) oriented to RRTs for planning in robotics and we illustrate it by presenting a model for generation of RRTs with holonomic limitations. We are working on the ENPS frame- work with the idea of moving towards a complete mobile robot system based on membrane systems, i.e. including controllers and planning; and we have incorporated new ingredients into the ENPS framework to meet the requirements of the RRT generation algorithm

A Review of Verbal and Non-Verbal Human-Robot Interactive Communication

In this paper, an overview of human-robot interactive communication is presented, covering verbal as well as non-verbal aspects of human-robot interaction. Following a historical introduction, and motivation towards fluid human-robot communication, ten desiderata are proposed, which provide an organizational axis both of recent as well as of future research on human-robot communication. Then, the ten desiderata are examined in detail, culminating to a unifying discussion, and a forward-looking conclusion

Evolution of Swarm Robotics Systems with Novelty Search

Novelty search is a recent artificial evolution technique that challenges traditional evolutionary approaches. In novelty search, solutions are rewarded based on their novelty, rather than their quality with respect to a predefined objective. The lack of a predefined objective precludes premature convergence caused by a deceptive fitness function. In this paper, we apply novelty search combined with NEAT to the evolution of neural controllers for homogeneous swarms of robots. Our empirical study is conducted in simulation, and we use a common swarm robotics task - aggregation, and a more challenging task - sharing of an energy recharging station. Our results show that novelty search is unaffected by deception, is notably effective in bootstrapping the evolution, can find solutions with lower complexity than fitness-based evolution, and can find a broad diversity of solutions for the same task. Even in non-deceptive setups, novelty search achieves solution qualities similar to those obtained in traditional fitness-based evolution. Our study also encompasses variants of novelty search that work in concert with fitness-based evolution to combine the exploratory character of novelty search with the exploitatory character of objective-based evolution. We show that these variants can further improve the performance of novelty search. Overall, our study shows that novelty search is a promising alternative for the evolution of controllers for robotic swarms.Comment: To appear in Swarm Intelligence (2013), ANTS Special Issue. The final publication will be available at link.springer.co