Incremental Online Learning in High Dimensions
Neural Computation
Constructive Incremental Learning from Only Local Information
Neural Computation
Neural Networks - 2006 Special issue: The brain mechanisms of imitation learning
Incremental learning of gestures by imitation in a humanoid robot
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Robotics: Modelling, Planning and Control
Robotics: Modelling, Planning and Control
Active learning with statistical models
Journal of Artificial Intelligence Research
Mimesis Model from Partial Observations for a Humanoid Robot
International Journal of Robotics Research
Online segmentation and clustering from continuous observation of whole body motions
IEEE Transactions on Robotics
Simultaneous tracking and balancing of humanoid robots for imitating human motion capture data
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Mimetic Communication Model with Compliant Physical Contact in Human-Humanoid Interaction
International Journal of Robotics Research
Correspondence Mapping Induced State and Action Metrics for Robotic Imitation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A robot learning from demonstration framework to perform force-based manipulation tasks
Intelligent Service Robotics
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We present an approach for kinesthetic teaching of motion primitives for a humanoid robot. The proposed teaching method starts with observational learning and applies iterative kinesthetic motion refinement using a forgetting factor. Kinesthetic teaching is supported by introducing the motion refinement tube, which represents an area of allowed motion refinement around the nominal trajectory. On the realtime control level, the kinesthetic teaching is handled by a customized impedance controller, which combines tracking performance with compliant physical interaction and allows to implement soft boundaries for the motion refinement. A novel method for continuous generation of motions from a hidden Markov model (HMM) representation of motion primitives is proposed, which incorporates time information for each state. The proposed methods were implemented and tested using DLR's humanoid upper-body robot Justin.