Biologically Inspired Framework for Learning and Abstract Representation of Attention Control
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Learning from demonstration in robots: Experimental comparison of neural architectures
Robotics and Computer-Integrated Manufacturing
Interactive imitation learning of object movement skills
Autonomous Robots
A nonparametric Bayesian approach toward robot learning by demonstration
Robotics and Autonomous Systems
Motor simulation via coupled internal models using sequential Monte Carlo
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
A syntactic approach to robot imitation learning using probabilistic activity grammars
Robotics and Autonomous Systems
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We propose a general architecture for action (mimicking) and program (gesture) level visual imitation. Action-level imitation involves two modules. The viewpoint transformation (VPT) performs a "rotation" to align the demonstrator's body to that of the learner. The visuo-motor map (VMM) maps this visual information to motor data. For program-level (gesture) imitation, there is an additional module that allows the system to recognize and generate its own interpretation of observed gestures to produce similar gestures/goals at a later stage. Besides the holistic approach to the problem, our approach differs from traditional work in i) the use of motor information for gesture recognition; ii) usage of context (e.g., object affordances) to focus the attention of the recognition system and reduce ambiguities, and iii) use iconic image representations for the hand, as opposed to fitting kinematic models to the video sequence. This approach is motivated by the finding of visuomotor neurons in the F5 area of the macaque brain that suggest that gesture recognition/imitation is performed in motor terms (mirror) and rely on the use of object affordances (canonical) to handle ambiguous actions. Our results show that this approach can outperform more conventional (e.g., pure visual) methods.