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Probabilistic movement modeling for intention inference in human–robot interaction
The Intention-Driven Dynamics Model is proposed to probabilistically model the generative process of movements that are directed by the intention and allows the intention to be inferred from observed movements using Bayes’ theorem. Expand
Probabilistic Modeling of Human Movements for Intention Inference
The Intention-Driven Dynamics Model (IDDM), a latent variable model for inferring unknown human intentions, is proposed and an efficient approximate inference algorithm to infer the human’s intention from an ongoing movement is introduced. Expand
Learning responsive robot behavior by imitation
This paper simultaneously record the movements of two humans engaged in on-going interaction tasks and learn compact models of the interaction that can thereafter be used by a robot to engage in a similar interaction with a human partner. Expand
Towards a Simulator for Imitation Learning with Kinesthetic Bootstrapping
A physics based simulator that allows kinesthetic interactions between a human and a robot to be recorded, and later used for imitation learning, and a new scheme for robot motion learning based on kinesthetic bootstrapping is proposed. Expand
A system for learning continuous human-robot interactions from human-human demonstrations
The effectiveness of the data-driven imitation learning system for learning human-robot interactions from human-human demonstrations on complex, sequential tasks is shown by presenting two applications involving collaborative human- robot assembly. Expand
Dynamic Mode Decomposition for perturbation estimation in human robot interaction
A machine learning approach which makes use of Dynamic Mode Decomposition (DMD) which is able to extract the dynamics of a nonlinear system and is well suited to separate noise from regular oscillations in sensor readings during cyclic robot movements under different behavior configurations is proposed. Expand
Towards Responsive Humanoids : Learning Interaction Models for Humanoid Robots
This extended abstract presents our ongoing work on deriving interaction models for humanoid robots. The approach differs from earlier interaction learning approaches in that it learns a model from aExpand
Cooperative Human-Robot Manipulation Tasks
Object manipulation tasks by humanoid robots often do not only involve the hands and upper limbs but require flexible control of whole body movements. Examples are cooperative transportation tasksExpand
Estimation of perturbations in robotic behavior using dynamic mode decomposition
Results of a user study show that the DMD-based machine learning approach can be used to design physical human–robot interaction techniques that not only result in robust robot behavior but also enjoy a high usability. Expand
Kinesthetic Bootstrapping: Teaching Motor Skills to Humanoid Robots through Physical Interaction
A new programming-by-demonstration method called Kinesthetic Bootstrapping for teaching motor skills to humanoid robots by means of intuitive physical interactions, which has been successfully applied to the learning of various complex motor skills such as walking and standing up. Expand