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.




[45]
Title: Interpolative robot control with the nested network approach IEEE Int. Symposium on Intelligent Control
Written by: Smagt P van der, Jansen A, Groen F
in: Aug. 1992
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on pages: 475-480
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Note:

Abstract: In the realm of pick-and-place problems, we aim at designing highly adaptive controllers which require minimum knowledge of the manipulator and its sensors. In this area, several models have proven more or less successful in one area or another. Non-neural parameter estimation techniques have been investigated, but real-time computational requirements grow out of bound when the number of state variables increases. The use of a single feed-forward network trained with conjugate gradient back-propagation gives fast and highly adaptive approximation, but needs up to ten feedback steps to get high-precision results. Kohonen networks give a precision up to 0.5cm.~with only two steps, but need thousands of iterations to attain reasonable results. Instead, we introduce the nested network method based on search trees which adapts in real-time and reaches a grasping precision of up to 1mm.~in only three steps.