Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Derivation of Primitives for Movement Classification
Autonomous Robots
Learning Context-Free Grammars with a Simplicity Bias
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Inducing Probabilistic Grammars by Bayesian Model Merging
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Recent Methods for RNA Modeling Using Stochastic Context-Free Grammars
CPM '94 Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching
The agent-based perspective on imitation
Imitation in animals and artifacts
Imitation in animals and artifacts
Recognizing multitasked activities from video using stochastic context-free grammar
Eighteenth national conference on Artificial intelligence
Natural methods for robot task learning: instructive demonstrations, generalization and practice
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Identifying hierarchical structure in sequences: a linear-time algorithm
Journal of Artificial Intelligence Research
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IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Effective robot task learning by focusing on task-relevant objects
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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Journal of Visual Communication and Image Representation
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Pattern Recognition
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PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
Robot Programming by Demonstration
Robot Programming by Demonstration
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ACM Transactions on Intelligent Systems and Technology (TIST)
View-invariant modeling and recognition of human actions using grammars
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
Visual learning by imitation with motor representations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning collaborative team behavior from observation
Expert Systems with Applications: An International Journal
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This paper describes a syntactic approach to imitation learning that captures important task structures in the form of probabilistic activity grammars from a reasonably small number of samples under noisy conditions. We show that these learned grammars can be recursively applied to help recognize unforeseen, more complicated tasks that share underlying structures. The grammars enforce an observation to be consistent with the previously observed behaviors which can correct unexpected, out-of-context actions due to errors of the observer and/or demonstrator. To achieve this goal, our method (1) actively searches for frequently occurring action symbols that are subsets of input samples to uncover the hierarchical structure of the demonstration, and (2) considers the uncertainties of input symbols due to imperfect low-level detectors. We evaluate the proposed method using both synthetic data and two sets of real-world humanoid robot experiments. In our Towers of Hanoi experiment, the robot learns the important constraints of the puzzle after observing demonstrators solving it. In our Dance Imitation experiment, the robot learns 3 types of dances from human demonstrations. The results suggest that under reasonable amount of noise, our method is capable of capturing the reusable task structures and generalizing them to cope with recursions.