E Elena Torta

Design of robust robotic proxemic behaviour

Personal robots that share the same space with humans need to be socially acceptable and effective as they interact with people. In this paper we focus our attention on the definition of a behaviour-based robotic architecture that, (1) allows the robot to navigate safely in a cluttered and dynamically changing domestic environment and (2) encodes embodied non-verbal interactions: the robot respects the users personal space by choosing the appropriate distance and direction of approach. The model of the personal space is derived from a human-robot psycho-physical study and it is described in a convenient mathematical form. The robots target location is dynamically inferred through the solution of a Bayesian filtering problem. The validation of the overall behavioural architecture shows that the robot is able to exhibit appropriate proxemic behaviour.

Evaluation of a Small Socially-Assistive Humanoid Robot in Intelligent Homes for the Care of the Elderly

The ageing population phenomenon is pushing the design of innovative solutions to provide assistance to the elderly. In this context a socially–assistive robot can act as a proactive interface in a smart-home environment, providing multimodal communication channels and generating positive feelings in users. The present paper reports results of a short term and a long term evaluation of a small socially assistive humanoid robot in a smart home environment. Eight elderly people tested an integrated smart–home robot system in five real–world scenarios. Six of the participants experienced the system in two sessions over a two week period; the other two participants had a prolonged experience of eight sessions over a three month period. Results showed that the small humanoid robot was trusted by the participants. A cross–cultural comparison showed that results were not due to the cultural background of the participants. The long term evaluation showed that the participants might engage in an emotional relationship with the robot, but that perceived enjoyment might decrease over time.

MODELING AND TESTING PROXEMIC BEHAVIOR FOR HUMANOID ROBOTS

Humanoid robots that share the same space with humans need to be socially acceptable and effective as they interact with people. In this paper we focus our attention on the definition of a behavior-based robotic architecture that (1) allows the robot to navigate safely in a cluttered and dynamically changing domestic environment and (2) encodes embodied non-verbal interactions: the robot respects the users personal space (PS) by choosing the appropriate distance and direction of approach. The model of the PS is derived from human–robot interaction tests, and it is described in a convenient mathematical form. The robots target location is dynamically inferred through the solution of a Bayesian filtering problem. The validation of the overall behavioral architecture shows that the robot is able to exhibit appropriate proxemic behavior.

Evaluation of Unimodal and Multimodal Communication Cues for Attracting Attention in Human–Robot Interaction

One of the most common tasks of a robot companion in the home is communication. In order to initiate an information exchange with its human partner, the robot needs to attract the attention of the human. This paper presents results of two user studies (

A model of the user's proximity for bayesian inference

\mathrm{N}=12

Study on adaptation of robot communication strategies in changing situations

N=12) to evaluate the effectiveness of unimodal and multimodal communication cues for attracting attention. Results showed that unimodal communication cues which involve sound generate the fastest reaction times. Contrary to expectations, multimodal communication cues resulted in longer reaction times with respect to the unimodal communication cue that produced the shortest reaction time.

Optimised Output Sensitivity Loop Shaping Controller for Ship Rudder Roll Damping

The care robot of the future should be able to navigate in domestic environments and perform meaningful tasks. Presumably, a robot that moves and interacts more intelligently gains more trust, is liked more and appears more humanlike. Here we test in three scenarios of differing urgency whether anticipatory walking behaviour of a robot is appreciated as more intelligent and whether this results in a more positive attitude towards the robot. We find no effect of walking behaviour and a main effect of urgency of the scenarios on perceived intelligence and on appropriateness. We interpret these results as that the type of interaction determines perceived intelligence and the attitude towards robots, but the degree of anticipation has no significant effect.

A Bayesian model for approaching a human

Embodied nonverbal cues are fundamental for regulating human-human social interactions. The physical embodiment of robots makes it likely that they will have to exhibit appropriate nonverbal interactive behaviors. In this paper we propose a model of the users proximity based on a superposition of quasi-Gaussian probability distributions which allows to express findings from HRI trials regarding distances and direction of approach in a human-robot interaction scenario. The way the model is formulated is suitable for well-established Bayesian filtering techniques, and thus the inference of the preferred distance and direction of approach in a human robot interaction scenario can be regarded as a state estimation problem. Results derived from simulations show the effectiveness of the inference process.