Fundamentals of speech recognition
Fundamentals of speech recognition
Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A mathematical theory of communication
ACM SIGMOBILE Mobile Computing and Communications Review
Assessing Image Features for Vision-Based Robot Positioning
Journal of Intelligent and Robotic Systems
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Common metrics for human-robot interaction
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Robotics and Computer-Integrated Manufacturing
Incremental learning of gestures by imitation in a humanoid robot
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Human-robot interaction: a survey
Foundations and Trends in Human-Computer Interaction
International Journal of Robotics Research
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
Robot Programming by Demonstration
Robot Programming by Demonstration
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Designing robot learners that ask good questions
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Applications of Artificial Intelligence in Safe Human–Robot Interactions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning to pour with a robot arm combining goal and shape learning for dynamic movement primitives
Robotics and Autonomous Systems
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
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This paper proposes an end-to-end learning from demonstration framework for teaching force-based manipulation tasks to robots. The strengths of this work are manyfold. First, we deal with the problem of learning through force perceptions exclusively. Second, we propose to exploit haptic feedback both as a means for improving teacher demonstrations and as a human---robot interaction tool, establishing a bidirectional communication channel between the teacher and the robot, in contrast to the works using kinesthetic teaching. Third, we address the well-known what to imitate? problem from a different point of view, based on the mutual information between perceptions and actions. Lastly, the teacher's demonstrations are encoded using a Hidden Markov Model, and the robot execution phase is developed by implementing a modified version of Gaussian Mixture Regression that uses implicit temporal information from the probabilistic model, needed when tackling tasks with ambiguous perceptions. Experimental results show that the robot is able to learn and reproduce two different manipulation tasks, with a performance comparable to the teacher's one.