The development of an android with convincingly lifelike appearance and behavior has been a long-standing goal in robotics, and recent years have seen great progress in many of the technologies needed to create such androids. However, it is necessary to actually integrate these technologies into a robot system in order to assess the progress that has been made towards this goal and to identify important areas for future work. To this end, we are developing ERICA, an autonomous android system capable of conversational interaction, featuring advanced sensing and speech synthesis technologies, and arguably the most humanlike android built to date. Although the project is ongoing, initial development of the basic android platform has been completed. In this paper we present an overview of the requirements and design of the platform, describe the development process of an interactive application, report on ERICA's first autonomous public demonstration, and discuss the main technical challenges that remain to be addressed in order to create humanlike, autonomous androids.

We present a tangible user interface based upon mediaBlocks: small, electronically tagged wooden blocks that serve as physical icons ("phicons") for the containment, transport, and manipulation of online media. MediaBlocks interface with media input and output devices such as video cameras and projectors, allowing digital media to be rapidly "copied" from a media source and "pasted" into a media display. MediaBlocks are also compatible with traditional GUIs, providing seamless gateways between tangible and graphical interfaces. Finally, mediaBlocks act as physical "controls" in tangible interfaces for tasks such as s e- quencing collections of media elements. CR Categories and Subject Descriptors: H.5.2 (User Interfaces) Input devices and strategies; H.5.1 (Multimedia Information Systems) Artificial, augmented, and virtual realities Additional Keywords: tangible user interface, tangible bits, phicons, physical constraints, ubiquitous compu ting

The uncanny valley theory proposed by Mori has been heavily investigated in the recent years by researchers from various fields. However, the videos and images used in these studies did not permit any human interaction with the uncanny objects. Therefore, in the field of human-robot interaction it is still unclear what, if any, impact an uncanny-looking robot will have in the context of an interaction. In this paper we describe an exploratory empirical study using a live interaction paradigm that involved repeated interactions with robots that differed in embodiment and their attitude towards a human. We found that both investigated components of the uncanniness (likeability and eeriness) can be affected by an interaction with a robot. Likeability of a robot was mainly affected by its attitude and this effect was especially prominent for a machine-like robot. On the other hand, merely repeating interactions was sufficient to reduce eeriness irrespective of a robot's embodiment. As a result we urge other researchers to investigate Mori's theory in studies that involve actual human-robot interaction in order to fully understand the changing nature of this phenomenon.

Recent studies in human-robot interaction (HRI) have investigated ways to harness the power of the crowd for the purpose of creating robot interaction logic through games and teleoperation interfaces. Sensor networks capable of observing human-human interactions in the real world provide a potentially valuable and scalable source of interaction data that can be used for designing robot behavior. To that end, we present here a fully automated method for reproducing observed real-world social interactions with a robot. The proposed method includes techniques for characterizing the speech and locomotion observed in training interactions, using clustering to identify typical behavior elements and identifying spatial formations using established HRI proxemics models. Behavior logic is learned based on discretized actions captured from the sensor data stream, using a naïve Bayesian classifier. Finally, we propose techniques for reproducing robot speech and locomotion behaviors in a robust way, despite the natural variation of human behaviors and the large amount of sensor noise present in speech recognition. We show our technique in use, training a robot to play the role of a shop clerk in a simple camera shop scenario, and we demonstrate through a comparison experiment that our techniques successfully enabled the generation of socially appropriate speech and locomotion behavior. Notably, the performance of our technique in terms of correct behavior selection was found to be higher than the success rate of speech recognition, indicating its robustness to sensor noise.