supported by
the STIFF
EC project
STIFF/VIACTORS Summer School on Impedance supported by
the VIACTORS
EC project
.



Keynote speakers:
Alin Albu-Schäffer, DLR
Antonio Bicchi, U. Pisa
Etienne Burdet, Imperial
Neville Hogan, MIT
Oussama Khatib, Stanford
Gerald Loeb, USC
Joseph McIntyre, UPD-CNRS
Patrick van der Smagt, DLR
Sethu Vijayakumar, Edinburgh

Etienne Burdet
Reader in Human Robotics
Faculty of Engineering
Dept. of Bioengineering
Imperial College London
South Kensington Campus
London SW7 2AZ
e.burdet(at)imperial.ac.uk

Kloster

video part 1
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video part 2
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Etienne Burdet

Robots can learn to control haptic interactions as humans do

In a series of studies over the last ten years, we have observed how the nervous system learns to coordinate muscles to produce both appropriate force and resistance to perturbation (mechanical impedance), at minimal metabolic cost. This is critical to understanding how we adapt motion in unstable situations in everyday life despite large motor noise, e.g., whenever we work with tools. To understand the mechanisms of this learning, we developed a computational model which predicts how sensory information is used to modify the motor commands to muscles during the whole learning process. We have recently implemented the human-like control algorithm on robot manipulators, which make them able to adapt force and impedance to interact efficiently with the environment, and to deal with inherent instability and forces. This novel human control algorithm opens new avenues for applications of robots interacting with humans, e.g. robotic rehabilitation, robotic wheelchair, or tremor attenuation. 

Etienne Burdet's main research interest is in human-machine interaction. He uses an approach integrating neuroscience and robotics to: i) investigate human motor control and ii) design efficient assistive devices and virtual reality based training for rehabilitation and surgery.


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Related publications

  1. E Burdet, G Gowrishankar, C Yang, A Albu-Schaeffer (2010). Learning Interaction Force, Impedance and Trajectory: by Humans, for Robots. Proc International Symposium on Experimental Robotics [pdf]

  2. C Yang, G Ganesh, S Haddadin, S Parusel, A Albu-Schaeffer and E Burdet (2011). Human like adaptation of force and impedance in stable and unstable interactions. Transactions on Robotics (in press)

  3. G Ganesh, H Haruno, M Kawato and E Burdet (2010). Motor memory and local minimization of error and effort, not global optimization, determine motor behavior. Journal of Neurophysiology 104 382-390. [pdf]

  4. DW Franklin, E Burdet, KP Tee, T Milner, R Osu and M Kawato (2008). CNS learns stable, accurate and efficient movements using a simple algorithm. Journal of Neuroscience 28 (44), 11165-11173. [pdf]

  5. E Burdet, R Osu, DW Franklin, TE Milner, M Kawato (2001). The CNS skillfully stabilizes unstable dynamics by learning optimal impedance. Nature 414 446-449. [pdf]