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Humanoid robots are arguably well suited to this. Sharing a similar morphology, they can communicate in a manner that supports the natural communication modalities of humans. Examples include facial expression, body posture, gesture, gaze direction, and voice. The ability for people to naturally communicate with these machines is important. However, for suitably complex environments and tasks, the ability for people to intuitively teach these robots will also be important. Social aspects enter profoundly into both of these challenging problem domains. The Sociable Machines Project develops an expressive anthropomorphic robot called Kismet that engages people in natural and expressive face-to-face interaction. Inspired by infant social development, psychology, ethology, and evolution, this work integrates theories and concepts from these diverse viewpoints to enable Kismet to enter into natural and intuitive social interaction with a human caregiver and to learn from them, reminiscent of parent-infant exchanges. To do this, Kismet perceives a variety of natural social cues from visual and auditory channels, and delivers social signals to the human caregiver through gaze direction, facial expression, body posture, and vocal babbles. The robot has been designed to support several social cues and skills that could ultimately play an important role in socially situated learning with a human instructor. These capabilities are evaluated with respect to the ability of naive subjects to read and interpret the robot's social cues, the robot's ability to perceive and appropriately respond to human social cues, the human's willingness to provide scaffolding to facilitate the robot's learning, and how this produces a rich, flexible, dynamic interaction that is physical, affective, social, and affords a rich opportunity for learning.
We gratefully acknowledge our sponsors for their generous support. The vision system was funded by a MURI research grant from ONR and DARPA. The social interaction work is funded in part by a MARS grant through DARPA and in part by NTT.
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