Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
ACM Computing Surveys (CSUR)
Factorial hidden Markov models and the generalized backfitting algorithm
Neural Computation
Human-Machine Collaborative Systems for Microsurgical Applications
International Journal of Robotics Research
Incremental learning of gestures by imitation in a humanoid robot
Proceedings of the ACM/IEEE international conference on Human-robot interaction
IEEE Transactions on Robotics
Online task recognition and real-time adaptive assistance for computer-aided machine control
IEEE Transactions on Robotics
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Human action learning via hidden Markov model
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
International Journal of Robotics Research
Incremental Learning and Memory Consolidation of Whole Body Human Motion Primitives
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Whole body motion primitive segmentation from monocular video
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Incremental clustering of gesture patterns based on a self organizing incremental neural network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Online segmentation and clustering from continuous observation of whole body motions
IEEE Transactions on Robotics
Comparative study of representations for segmentation of whole body human motion data
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Recognition of affect based on gait patterns
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A biomimetic approach to inverse kinematics for a redundant robot arm
Autonomous Robots
Machine learning approaches for time-series data based on self-organizing incremental neural network
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Task-specific generalization of discrete and periodic dynamic movement primitives
IEEE Transactions on Robotics
Human motion database with a binary tree and node transition graphs
Autonomous Robots
Learning Non-linear Multivariate Dynamics of Motion in Robotic Manipulators
International Journal of Robotics Research
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Conceptual Imitation Learning in a Human-Robot Interaction Paradigm
ACM Transactions on Intelligent Systems and Technology (TIST)
International Journal of Robotics Research
A Self-Training Approach for Visual Tracking and Recognition of Complex Human Activity Patterns
International Journal of Computer Vision
Estimating the non-linear dynamics of free-flying objects
Robotics and Autonomous Systems
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Towards the detection of unusual temporal events during activities using HMMs
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
A robot learning from demonstration framework to perform force-based manipulation tasks
Intelligent Service Robotics
Skill learning and inference framework
AGI'13 Proceedings of the 6th international conference on Artificial General Intelligence
Discriminative functional analysis of human movements
Pattern Recognition Letters
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This paper describes a novel approach for autonomous and incremental learning of motion pattern primitives by observation of human motion. Human motion patterns are abstracted into a dynamic stochastic model, which can be used for both subsequent motion recognition and generation, analogous to the mirror neuron hypothesis in primates. The model size is adaptable based on the discrimination requirements in the associated region of the current knowledge base. A new algorithm for sequentially training the Markov chains is developed, to reduce the computation cost during model adaptation. As new motion patterns are observed, they are incrementally grouped together using hierarchical agglomerative clustering based on their relative distance in the model space. The clustering algorithm forms a tree structure, with specialized motions at the tree leaves, and generalized motions closer to the root. The generated tree structure will depend on the type of training data provided, so that the most specialized motions will be those for which the most training has been received. Tests with motion capture data for a variety of motion primitives demonstrate the efficacy of the algorithm.