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This paper reports the application of results from human- social agent interaction experiments to inform the design of a social robot to monitor levels of pollutive gasses in the air. Next to licensed environmental agents and immobile chemical sensors, mobile technologies such as robotic agents are needed to collect complaints and smell descriptions from humans in urban industrial areas. These robots will interact with members of the public and ensure responsiveness and accuracy of responses. For robots to be accepted as representative environmental monitoring agents and for people to comply to robot instructions in the case of a calamity, social skills will be important. In this paper we will describe the intelligent environment the environmental robot is part of and discuss preliminary work on the effects of robot empathic and touch behaviors on human responses to robots. These and future findings will inform the design of social monitoring robot behaviors in public settings. [ABSTRACT FROM AUTHOR]

Robot games have been proposed as a way to motivate people to do physical exercises while playing. Although this area is very new, both commercial and scientific robot games have been developed mainly based on interaction with a single user and a robot. The goal of this paper is to describe a generic software framework which can be used to create games where multiple players can play against a mobile robot. The paper shows how an adaptive AI system (D2) developed for real-time strategy (RTS) computer games can be successfully applied in a robotics context using the robotics control framework Player/Stage. D2 is based on Case-Based Planning which learns from demonstration. Using the proposed framework, the paper shows how a robot learns a strategy for an implementation of a simple game. [ABSTRACT FROM AUTHOR]

In this article a hand gesture recognition system that allows interacting with a service robot, in dynamic environments and in real-time, is proposed. The system detects hands and static gestures using cascade of boosted classifiers, and recognize dynamic gestures by computing temporal statistics of the hand΄s positions and velocities, and classifying these features using a Bayes classifier. The main novelty of the proposed approach is the use of context information to adapt continuously the skin model used in the detection of hand candidates, to restrict the image΄s regions that need to be analyzed, and to cut down the number of scales that need to be considered in the hand-searching and gesture-recognition processes. The system performance is validated in real video sequences. In average the system recognized static gestures in 70% of the cases, dynamic gestures in 75% of them, and it runs at a variable speed of 5-10 frames per second. [ABSTRACT FROM AUTHOR]

This paper describes a comprehensive approach to extract a common feature set from the image sequences. We use simple features which are easily extracted from a 3D wireframe model and efficiently used for different applications on a benchmark database. Features verstality is experimented on facial expressions recognition, face reognition and gender classification. We experiment different combinations of the features and find reasonable results with a combined features approach which contain structural, textural and temporal variations. The idea follows in fitting a model to human face images and extracting shape and texture information. We parametrize these extracted information from the image sequences using active appearance model (AAM) approach. We further compute temporal parameters using optical flow to consider local feature variations. Finally we combine these parameters to form a feature vector for all the images in our database. These features are then experimented with binary decision tree (BDT) and Bayesian Network (BN) for classification. We evaluated our results on image sequences of Cohn Kanade Facial Expression Database (CKFED). The proposed system produced very promising recognition rates for our applications with same set of features and classifiers. The system is also realtime capable and automatic. [ABSTRACT FROM AUTHOR]

In this paper we propose an Entertainment Humanoid Robot model based on Latent Semantic Analysis, that tries to exhibit an emotional behavior in the interaction with human. Latent Semantic Analysis (LSA), based on vector space allows the coding of the words semantics by specific statistical computations applied to a large corpus of text. We illustrate how the creation and the use of this emotional conceptual space can provide a framework upon which to build ˵Latent Semantic Behavior″ because it simulates the emotional-associative capabilities of human beings. This approach integrates traditional knowledge representation with intuitive capabilities provided by geometric and sub-symbolic information modeling. To validate the effectiveness of our approach we have simulated an Humanoid Robot Robovie-M on dInfoBots a linux based framework developed in our Mobile Robot Lab. [ABSTRACT FROM AUTHOR]

The use of master-slave surgical robots for Minimally Invasive Surgery (MIS) has created a physical separation between the surgeon and the patient. Reconnecting the essential visuomotor sensory feedback is important for the safe practice of robotic assisted MIS procedures. This paper introduces a novel gaze contingent framework with real-time haptic feedback by transforming visual sensory information into physical constraints that can interact with the motor sensory channel. We demonstrate how motor tracking of deforming tissue can be made more effective and accurate through the concept of gaze-contingent motor channelling. The method also uses 3D eye gaze to dynamically prescribe and update safety boundaries during robotic assisted MIS without prior knowledge of the soft-tissue morphology. Initial validation results on both simulated and robotic assisted phantom procedures demonstrate the potential clinical value of the technique. [ABSTRACT FROM AUTHOR]

Learning by imitation enables people to program robots simply by showing them what to do, instead of having to specify the motor commands of the robot. To achieve imitative behaviour in a simulated robot, a modular connectionist architecture for motor learning and control was implemented. The architecture was used to imitate human dance movements. The architecture self-organizes the decomposition of the movement to be imitated across different modules. The results show that the decomposition of the movement tends to be both competitive (i.e. one module dominates the others for a part of the movement) and collaborative (i.e. modules cooperate in controlling the robot). [ABSTRACT FROM AUTHOR]