DocumentCode
1928599
Title
Investigating models of social development using a humanoid robot
Author
Scassellati, Brian
Author_Institution
Dept. of Comput. Sci., Yale Univ., New Haven, CT, USA
Volume
4
fYear
2003
fDate
20-24 July 2003
Firstpage
2704
Abstract
Human social dynamics rely upon the ability to correctly attribute beliefs, goals, and percepts to other people. The set of abilities that allow an individual to infer these hidden mental states based on observed actions and behavior has been called a "theory of mind". Drawing from the models of Baron-Cohen (1995) and Leslie (1994), a novel architecture called embodied theory of mind was developed to link high-level cognitive skills to the low-level perceptual abilities of a humanoid robot. The implemented system determines visual saliency based on inherent object attributes, high-level task constraints, and the attentional states of others. Objects of interest are tracked in real-time to produce motion trajectories which are analyzed by a set of naive physical laws designed to discriminate animate from inanimate movement. Animate objects can be the source of attentional states (detected by finding faces and head orientation) as well as intentional states (determined by motion trajectories between objects). Individual components are evaluated by comparisons to human performance on similar tasks, and the complete system is evaluated in the context of a basic social learning mechanism that allows the robot to mimic observed movements.
Keywords
cognition; humanoid robots; robot vision; animate objects; attentional states; embodied theory of mind; hidden mental states; high-level cognitive skills; high-level task constraints; human social dynamics; humanoid robot; low-level perceptual abilities; motion trajectories; object attributes; observed movement mimicking; social development models; social learning mechanism; visual saliency; Face detection; Facial animation; Humanoid robots; Humans; Learning systems; Motion analysis; Motion detection; Object detection; Tracking; Trajectory;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-7898-9
Type
conf
DOI
10.1109/IJCNN.2003.1223995
Filename
1223995
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