Picture of  Patrick van der Smagt

Patrick van der Smagt

current: Head of AI Research, data lab, VW Group

Previous: Director of BRML labs
fortiss, An-Institut der Technischen Universität München
Professor for Biomimetic Robotics and Machine Learning, TUM

Chairman of Assistenzrobotik e.V.
addressGermany
emailsmagtbrmlorg

In heading a research lab focussing on machine learning and its application in robotics, biomimetics and sensory data processing, my goal is to develop the techniques to model and use (human) movement.

The slides of my Keynote on end-to-end learning at the 2015 IROS conference are available here.

Visit my blog.

awards

Best Paper Award, Int Conf on Neural Information Processing (ICONIP 2014)
King-Sun Fu Best 2013 Transactions on Robotics Paper Award (2014)
Harvard Medical School/MGH Martin Research Prize (2013)
Erwin Schrödinger Award, Helmholtz Gesellschaft (2012)
SfN BCI Award Finalist (2012)
TUM Leonardo da Vinci Award (2008)
IEEE Best Paper Awards
Beckmann Institute Fellowship (1995)
NACEE Fellowship

in the press

various sources, e.g. NY Times, May 16, 2012: on a brain-controlled robotics experiment
Bayerische Rundfunk, May, 2012: "Wenn Rechner immer intelligenter werden", Radio Wissen
n-tv, June 10, 2010: EMG-controlled robotics
pinc, May 18, 2009, "biorobotics"
Het Financieele Dagblad, May, 2009
Discovery Channel, April 2009: "Future Homes"
3Sat, April 22, 2007: "Z wie Zukunft"
RTLII, March, 2007: "Welt der Wunder"
Pro7, Nov. 5, 2006: "Wunderwelt Wissen"
Abendzeitung, Oct. 28, 2006: Bestnoten für Forscher und Unternehmen
ZDF: Heute Journal, Sep. 15, 2006: Interview
ORF: "Newton" Science report, April 30, 2006: report on advanced prostheses
Süddeutsche Zeitung, Mar. 03, 2006: "Künstliche Hand am Computer entwickelt"
Süddeutsche Zeitung, Jan. 26, 2006: "Direkter Draht zum Hirn"

We were and are funded by various sources, including:
DFG project "SPP autonomous learning" (2012-2015)
NEUROBOTICS (EC project, 2005-2009)
NINAPRO (Swiss project, 2010-2013)
SENSOPAC (EC project, 2006-2010)
STIFF (EC project, 2009-2011)
THE (EC project, 2010-2014)
VIACTORS (EC project, 2009-2012)

on publishing

In June 2012 I resigned as editor of Neural Networks (Elsevier). Having worked with Neural Networks for almost 20 years, I have come to realise that the publication model propagated by behind-paywall publishers no longer combines with my own views of publication DOs and DONTs.  In particular, I have decided to move away from classical publication methods towards open access publishing, now that such alternatives are maturing.

After so many years of research and publishing, it is clear that only a peer-to-peer (double-open) review system with open access to the publications can be fair and unbiased.

I am currently still editor of Biological Cybernetics, as the open access model is being supported by Springer. However, also that large publishing house will have to rethink their approach to scientific publication before long.

Reviewing is good.  But open publication is an alternative.  My blog is an attempt to solve, for my own benefit, this publication issue.




[15]
Title: Neural network control of a pneumatic robot arm
Written by: Hesselroth T, Sarkar K, Smagt P van der, Schulten K
in: IEEE Transactions on Systems, Man, and Cybernetics January 1994
Volume: 24 Number: 1
on pages: 28--38
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DOI: 10.1109/21.259683
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Abstract: A neural map algorithm has been employed to control a five-joint pneumatic robot arm and gripper through feedback from two video cameras. The pneumatically driven robot arm (SoftArm) employed in this investigation shares essential mechanical characteristics with skeletal muscle systems. To control the position of the arm, 200 neurons formed a network representing the three-dimensional workspace embedded in a four-dimensional system of coordinates from the two cameras, and learned a three-dimensional set of pressures corresponding to the end effector positions, as well as a set of 3x4 Jacobian matrices for interpolating between these positions. The gripper orientation was achieved through adaptation of a 1x4 Jacobian matrix for a fourth joint. Because of the properties of the rubber-tube actuators of the SoftArm, the position as a function of supplied pressure is nonlinear, nonseparable, and exhibits hysteresis. Nevertheless, through the neural network learning algorithm the position could be controlled to an accuracy of about one pixel (3 mm) after two hundred learning steps and the orientation could be controlled to two pixels after eight hundred learning steps. This was achieved through employment of a linear correction algorithm using the Jacobian matrices mentioned above. Applications of repeated corrections in each positioning and grasping step leads to a very robust control algorithm since the Jacobians learned by the network have to satisfy the weak requirement that the Jacobian yields a reduction of the distance between gripper and target.