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Max - A Multimodal Assistant in Virtual Reality Construction
The overall architecture of Max is described with a focus on dialog and interaction management and the generation of synchronized multimodal utterances.
Physical Human-Robot Interaction: Mutual Learning and Adaptation
- Shuhei Ikemoto, H. B. Amor, T. Minato, B. Jung, H. Ishiguro
- Computer ScienceIEEE Robotics & Automation Magazine
- 29 February 2012
It is shown that this algorithm helps to improve the quality of the interaction between a robot and a human caregiver and two human-in-the-loop learning scenarios that are inspired by human parenting behavior are presented.
Grasp Recognition with Uncalibrated Data Gloves - A Comparison of Classification Methods
- Guido Heumer, H. B. Amor, Matthias Weber, B. Jung
- Computer ScienceIEEE Virtual Reality Conference
- 10 March 2007
It is shown that a reasonably well to highly reliable recognition of grasp types can be achieved - depending on whether or not the glove user is among those training the classifier - even with uncalibrated data gloves, and the best performing classification methods are identified.
FlurMax: An Interactive Virtual Agent for Entertaining Visitors in a Hallway
FlurMax, a virtual agent, inhabits a hallway at the University of Bielefeld. He resides in a wide-screen panel equipped with a video camera to track and interact with visitors using speech, gesture,…
Learning responsive robot behavior by imitation
- H. B. Amor, David Vogt, Marco Ewerton, Erik Berger, B. Jung, Jan Peters
- Computer ScienceIEEE/RSJ International Conference on Intelligent…
- 1 November 2013
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.
Grasp synthesis from low‐dimensional probabilistic grasp models
A novel data‐driven animation method for the synthesis of natural looking human grasping that greatly reduces the high number of degrees of freedom of the human hand to a few dimensions in a continuous grasp space.
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.
A system for learning continuous human-robot interactions from human-human demonstrations
- David Vogt, Simon Stepputtis, Steve Grehl, B. Jung, H. B. Amor
- Computer ScienceIEEE International Conference on Robotics and…
- 1 May 2017
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.
Grasp Recognition for Uncalibrated Data Gloves: A Machine Learning Approach
- Guido Heumer, H. B. Amor, B. Jung
- Computer SciencePRESENCE: Teleoperators and Virtual Environments
- 1 April 2008
It is shown that a reasonably good to highly reliable recognition of grasp types can be achieved depending on whether or not the glove user is among those training the classifiereven with uncalibrated data gloves, and the best performing classification methods for the recognition of various grasp types are identified.
Inferring guidance information in cooperative human-robot tasks
- Erik Berger, David Vogt, Nooshin HajiGhassemi, B. Jung, Heni Ben Amor
- Computer Science13th IEEE-RAS International Conference on…
- 1 October 2013
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.