Hidden Markov model analysis of force/torque information in telemanipulation
International Journal of Robotics Research
Kinematic models for model-based compliant motion in the presence of uncertainty
International Journal of Robotics Research - Special issue on integration among planning, sensing, and control
Robot Motion Planning
A Mathematical Introduction to Robotic Manipulation
A Mathematical Introduction to Robotic Manipulation
Gesture-Based Programming for Robotics: Human-Augmented Software Adaptation
IEEE Intelligent Systems
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Geometric Fundamentals of Robotics (Monographs in Computer Science)
Geometric Fundamentals of Robotics (Monographs in Computer Science)
International Journal of Robotics Research
International Journal of Robotics Research
Integration of planning and execution in force controlled compliant motion
Robotics and Autonomous Systems
International Journal of Robotics Research
Automatic Generation of High-level Contact State Space between 3D Curved Objects
International Journal of Robotics Research
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
IEEE Transactions on Robotics
IEEE Transactions on Robotics
Online statistical model recognition and State estimation for autonomous compliant motion
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental Learning of Tasks From User Demonstrations, Past Experiences, and Vocal Comments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A robot learning from demonstration framework to perform force-based manipulation tasks
Intelligent Service Robotics
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Robot programming by demonstration is a robot programming paradigm in which a human operator directly demonstrates the task to be performed. In this paper, we focus on programming by demonstration of compliant motion tasks, which are tasks that involve contacts between an object manipulated by the robot and the environment in which it operates. Critical issues in this paradigm are to distinguish essential actions from those that are not relevant for the correct execution of the task and to transform this information into a robot-independent representation. Essential actions in compliant motion tasks are the contacts that take place, and therefore, it is important to understand the sequence of contact states that occur during a demonstration, called contact classification or contact segmentation. We propose a contact classification algorithm based on a supervised learning algorithm, in particular on a stochastic gradient boosting algorithm. The approach described in this paper is accurate and does not depend on the geometric model of the objects involved in the demonstration. It neither relies on the kinestatic model of the contact interactions nor on the contact state graph, whose computation is usually of prohibitive complexity even for very simple geometric object models.