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Probabilistic movement modeling for intention inference in human–robot interaction
TLDR
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.
View on SAGE
Probabilistic Modeling of Human Movements for Intention Inference
TLDR
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.
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Learning responsive robot behavior by imitation
TLDR
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.
View on IEEE
Towards a Simulator for Imitation Learning with Kinesthetic Bootstrapping
TLDR
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.
A system for learning continuous human-robot interactions from human-human demonstrations
TLDR
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.
View on IEEE
Inferring guidance information in cooperative human-robot tasks
TLDR
A machine learning approach based on sensor data, such as accelerometer and pressure sensor information, is proposed for cooperative tasks between a human and a humanoid NAO robot and demonstrates the feasibility of this approach.
View on IEEE
Dynamic Mode Decomposition for perturbation estimation in human robot interaction
TLDR
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.
View on IEEE
Learning Two-Person Interaction Models for Responsive Synthetic Humanoids
TLDR
This paper introduces a new imitation learning approach that is based on the simultaneous motion capture of two human interaction partners that allows the real-time generation of agent behaviors that are responsive to the body movements of an interaction partner.
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Towards Responsive Humanoids : Learning Interaction Models for Humanoid Robots
TLDR
The ongoing work on deriving interaction models for humanoid robots differs from earlier interaction learning approaches in that it learns a model from a recorded rapport between two human individuals.
Estimation of perturbations in robotic behavior using dynamic mode decomposition
TLDR
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.
View on Taylor & Francis
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