NCCR Robotics is a consortium of robotics laboratories across Switzerland, working on robots for improving the quality of life and to strengthen robotics in Switzerland and worldwide. Newsletter
People
NCCR Robotics is organised around a number of research groups and labs. The lead members of each lab are shown below, follow the links to learn more about each… Read more
Robots for Education
While the use of technology has now become so useful in most jobs, our society need to move towards better education in technology in general and robotics in particular,… Read more
Past Events
| Date/Time | Event | Description |
|---|---|---|
|
29 Oct – 31 Oct 2018 9:00 am – 5:00 pm |
Conference on Robot Learning (CoRL 2018) | CoRL 2018 will take place on October 29-31 2018 in Zurich. The conference focuses on the intersection of robotics and machine learning. CoRL aims at being a selective, top-tier venue... |
Zurich: Machine learning
Publication date: February 27, 2017
Published in: Business Traveller
Allegro Hand
Publication date: February 21, 2017
Published in: Discovery Channel Canada
Link: http://www.discovery.ca/
Video Friday – Allegro Hand
Publication date: February 17, 2017
Published in: IEEE Spectrum
Can you feel what I feel?
Publication date: September 23, 2016
Published in: Science Magazine
Prof. Robert Riener NCCR Co-Director & Professor Management Team and Sensory-Motor Systems Lab (SMS), ETH Zurich riener@hest.ethz.ch +41 44 632 66 79
Dr. Jaime Duarte Postdoctoral Researcher Sensory-Motor Systems Lab (SMS), ETH Zurich jaime.duarte@hest.ethz.ch +41 44 632 57 95
Marko Bjelonic PhD Student Robotic Systems Lab (RSL), ETH Zurich marko.bjelonic@mavt.ethz.ch +41 44 632 21 45
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EMG-based decoding of grasp gestures in reaching-to-grasping motions
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Predicting the grasping function during reach-to-grasp motions is essential for controlling a prosthetic hand or a robotic assistive device. An early accurate prediction increases the usability and the comfort of a prosthetic device. This work proposes an electromyographic-based learning approach that decodes the grasping intention at an early stage of reach-to-grasp motion, i.e. before the final grasp/hand pre-shape takes place. Superficial electrodes and a Cyberglove were used to record the arm muscle activity and the finger joints during reach-to-grasp motions. Our results showed a 90% accuracy for the detection of the final grasp about 0.5 s after motion onset. This paper also examines the effect of different objects’ distances and different motion speeds on the detection time and accuracy of the classifier. The use of our learning approach to control a 16-degrees of freedom robotic hand confirmed the usability of our approach for the real-time control of robotic devices.
Posted on: January 20, 2017