A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
The mirror system, imitation, and the evolution of language
Imitation in animals and artifacts
Challenges in building robots that imitate people
Imitation in animals and artifacts
International Journal of Robotics Research
A BIOLOGICALLY INSPIRED METHOD FOR CONCEPTUAL IMITATION USING REINFORCEMENT LEARNING
Applied Artificial Intelligence
Online segmentation and clustering from continuous observation of whole body motions
IEEE Transactions on Robotics
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Planning for human-robot teaming in open worlds
ACM Transactions on Intelligent Systems and Technology (TIST)
Human-aware task planning: An application to mobile robots
ACM Transactions on Intelligent Systems and Technology (TIST)
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In general, imitation is imprecisely used to address different levels of social learning from high-level knowledge transfer to low-level regeneration of motor commands. However, true imitation is based on abstraction and conceptualization. This article presents a model for conceptual imitation through interaction with the teacher to abstract spatio-temporal demonstrations based on their functional meaning. Abstraction, concept acquisition, and self-organization of proto-symbols are performed through an incremental and gradual learning algorithm. In this algorithm, Hidden Markov Models (HMMs) are used to abstract perceptually similar demonstrations. However, abstract (relational) concepts emerge as a collection of HMMs irregularly scattered in the perceptual space but showing the same functionality. Performance of the proposed algorithm is evaluated in two experimental scenarios. The first one is a human-robot interaction task of imitating signs produced by hand movements. The second one is a simulated interactive task of imitating whole body motion patterns of a humanoid model. Experimental results show efficiency of our model for concept extraction, proto-symbol emergence, motion pattern recognition, prediction, and generation.