Abstract not found

Human speech provides a natural and intuitive interface for both communicating with humanoid robots as well as for teaching them. In general, the acoustic pattern of speech contains three kinds of information: who the speaker is, what the speaker said, and how the speaker said it. This paper focuses on the question of recognizing affective communicative intent in robot-directed speech. We present an approach for recognizing four distinct prosodic patterns that communicate praise, prohibition, attention, and comfort to preverbal infants. These communicative intents are well matched to teaching a robot since praise, prohibition, and directing the robot’s attention to relevant aspects of a task, could be used by a human instructor to intuitively facilitate the robot’s learning process. We integrate this perceptual ability into our robot’s ”emotion ” system, thereby allowing a human to directly manipulate the robot’s affective state. This has a powerful organizing influence on the robot’s behavior, and will ultimately be used to socially communicate affective reinforcement. Communicative efficacy has been tested with people very familiar with the robot as well as with naive subjects.

This paper presents design research conducted as part of a larger project on human-robot interaction. The primary goal of this study was to come to an initial understanding of what features and dimensions of a humanoid robot's face most dramatically contribute to people's perception of its humanness. To answer this question we analyzed 48 robots and conducted surveys to measure people's perception of each robot’s humanness. Through our research we found that the presence of certain features, the dimensions of the head, and the total number of facial features heavily influence the perception of humanness in robot heads. This paper presents our findings and initial guidelines for the design of humanoid robot heads.

Currently, we are experiencing the rise of a new interface paradigm: Robotic User Interfaces (RUI). We give a structured overview of current studies and implementations in the area of RUI and discuss their dis/advantages. In particular we classify the RUIs in terms of tool – toy scale, remote control – autonomy scale, reactive dialogue scale and anthropomorphism scale. Unfortunately, many of the robots currently celebrated in the media are not particular interesting for the area of RUI because they only resemble advanced remote controlled toys. Next we describe Muu and eMuu, two conversational robotic user interfaces developed at the Media Integration and Communications Group (MIC) of the Advanced Telecommunications Research Institute International (ATR). The research scope of Muu is dialogue coordination in mixed initiative situations with multiple participants and eMuu’s scope is the communication of emotions. We provide a short technical introduction to the robots and describe our research questions. Next we report on an upcoming experiment in which investigate user’s interaction with eMuu.

A salient feature of the ambient intelligent home of the future will be the natural interaction between the home and its inhabitants through speech. An embodied home character is necessary to ensure a natural dialogue by continuously providing intuitive feedback in the form of conversational and emotional body language. This study experimentally investigates the influence of the character's embodiment (screen character and robotic character) and its emotional expressiveness on the enjoyability of the interaction. The presence of emotional expressions significantly increased the enjoyability of the interaction with the robotic character. The embodiment had no significant influence on the enjoyability. However, in the robotic character condition a social facilitation effect and a high forgiveness for speech recognition errors was observed.

The existence of emotion and cognition as two interacting systems, both with important roles in decision-making, has been recently advocated by neurophysiological research (LeDoux, 1998, Damasio, 1994). Following that idea, this paper presents the ALEC agent architecture which has both emotive and cognitive learning, as well as emotive and cognitive decision-making capabilities to adapt to real-world environments. These two learning mechanisms embody very different properties which can be related to those of natural emotion and cognition systems. The reported

In this paper, an architecture for an emotion-based agent and its application to a real robot, situated in a semistructured environment, is presented. This architecture corresponds to an improved version of the one proposed by Maçãs et. al.. Both were developed based on a particular interpretation of the neuro-physiological findings of Damásio and LeDoux, namely the concepts of stimuli parallel processing, by LeDoux, and somatic marking, by Damásio. Given several applications of this architecture with virtual agents and in simulated environments, the goal here was to study and evaluate the utility and efficiency of this emotion-based agent architecture with real robots and real environments.

As robots take on an increasingly ubiquitous role in society, they must be easy for the average citizen to use and interact with. They must also appeal to persons of different age, gender, income, education, and so forth. This raises the important question of how to properly interface untrained humans with these sophisticated technologies in a manner that is intuitive, efficient, and enjoyable to use. Ideally, robots

Abstract. Robots have entered our domestic lives, but yet, little is known about their impact on the home. This paper takes steps towards addressing this omission, by reporting results from an empirical study of iRobot’s Roomba™, a vacuuming robot. Our findings suggest that, by developing intimacy to the robot, our participants were able to derive increased pleasure from cleaning, and expended effort to fit Roomba into their homes, and shared it with others. These findings lead us to propose four design implications that we argue could increase people’s enthusiasm for smart home technologies.

The recognition of user intentions is an important feature for humanoid robots to make implicit and human-like interactions possible. In this paper, we introduce a formal view on user-intentions in human-machine interaction and how they can be estimated by observing user actions. We use Hybrid Dynamic Bayesian Networks to develop a generic model that includes connections between intentions, actions, and sensor measurements. This model can be used to extend arbitrary human-machine applications by intention recognition. 1