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Sebastian Höfer

Lupe


Room: MAR 5.065
Telephone: +49.30.314-73118
Office hours: upon request

my blog on machine intelligence

me@Twitter
me@Google scholar    

News

04/05/2016

Our paper about our lessons learned from the Amazon Picking Challenge 2015 has been accepted at RSS 2016! Yay! You can find the paper here (PDF, 3,9 MB).

Update 23/07/2016: Our paper has won the Best Systems Paper award! Congrats to my colleagues and co-authors!

03/05/2016

I have made my slides for yesterday's talk about Learning with Side Information at the Berlin Machine Learning Group Meetup available online.

08/04/2016

I'll be giving a talk about Learning with Side Information at the Berlin Machine Learning Group Meetup on May 2nd, 7pm at Amazon ML. Unfortunately the event is already fully booked, but there is a waitlist available. (But the main reason for this is probably the free pizza, not my talk ;-)

29/02/2016

We have made the paper describing the perception system of our winning entry to the Amazon Picking Challenge 2015 available as a technical report:

Probabilistic Multi-Class Segmentation for the Amazon Picking Challenge

18/02/2016

Final version of our ICRA16 paper on integrated tracking, segmentation and shape reconstruction of articulated objects is online:

An Integrated Approach to Visual Perception of Articulated Objects (PDF, 2,3 MB)

10/02/2016

Paper and code for our paper on learning with side information are available on arXiv/github:

arXiv preprint: Patterns for Learning with Side Information

concarne on github

Research Interests

I am interested in robots who learn autonomously from their own interaction how to successfully manipulate their environment. Learning happens on many levels: I am particularly interested in the boundary between low-level sensorimotor learning and high-level reasoning. My recent work deals with the autonomous acquisition of high-level relational models for discovery of an object's degrees of freedom [1] (PDF, 400,5 KB), and methods to learn about the position and shape of articulated objects from vision [2].

More recently, I got interested in how additional modalities allow to learn more task-specific representations in order to make learning more effective [3].

Previously I have been working at the labs of Luc Steels at Sony CSL Paris and the VUB in Brussels on evolutionary linguistics (c.f. this introductory paper). I was working on a project dealing with autonomous grounding of action language [4], and linguistics projects on computational modeling of grammatical agreement [5] (PDF, 729,0 KB) as well as of complex grammatical features of Polish [6].

For my master thesis, I was working at the Neurorobotics lab under the guidance of Manfred Hild. I was studying the question how a humanoid robot can acquire a compact representation [7] of its prioprioceptive sensory input, using Slow Feature Analysis (c.f. Scholarpedia). The main goal was to learn to classify the robot's postures in an unsupervised manner.

CV

Publications

Rico Jonschkowski and Clemens Eppner and Sebastian Höfer and Roberto Martín-Martín and Oliver Brock. Probabilistic Multi-Class Segmentation for the Amazon Picking Challenge. Technical Report RBO-2016-01 , Department of Computer Engineering and Microelectronics, Technische Universität Berlin, 2016.

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Clemens Eppner and Sebastian Höfer and Rico Jonschkowski and Roberto Martín-Martín and Arne Sieverling and Vincent Wall and Oliver Brock. Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems. Robotics: Science and Systems (RSS), pp. accepted, to be published, 2016.

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Rico Jonschkowski and Sebastian Höfer and Oliver Brock. Patterns for Learning with Side Information. arXiv:1511.06429 [cs.LG] : 2016.

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Roberto Martín-Martín and Sebastian Höfer and Oliver Brock. An Integrated Approach to Visual Perception of Articulated Objects. Proceedings of the IEEE International Conference on Robotics and Automation :accepted, to be published, 2016.

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Marcus Buckmann and Robert Gaschler and Sebastian Höfer and Dennis Loeben and Peter A. Frensch and Oliver Brock. Learning to Explore the Structure of Kinematic Objects in a Virtual Environment. Frontiers in Psychology 6(374): 2015.

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Malte Lorbach and Sebastian Höfer and Oliver Brock. Prior-Assisted Propagation of Spatial Information for Object Search. IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2904-2909, 2014.

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Sebastian Höfer and Tobias Lang and Oliver Brock. Extracting Kinematic Background Knowledge from Interactions Using Task-Sensitive Relational Learning. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 4342-4347, 2014.

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Sebastian Höfer and Oliver Brock. Learning Compact Relational Models for the Exploration of Articulated Objects. Proceedings of the ICRA Mobile Manipulation Workshop on Interactive Perception (ICRA), 2013.

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Luc Steels and Michael Spranger and Remi van Trijp and Sebastian Höfer and Manfred Hild. Emergent Action Language on Real Robots. Language Grounding in Robots. Springer (US), chap. 13, 255-276, 2012.

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Katrien Beuls and Luc Steels and Sebastian Höfer. The Emergence of Internal Agreement Systems. Experiments in Cultural Language Evolution, pp. 233-256, 2012.

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Sebastian Höfer and Michael Spranger and Manfred Hild. Posture Recognition Based on Slow Feature Analysis. Language Grounding in Robots. Springer Verlag, chap. 06, 111-130, 2012.

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Katrien Beuls and Sebastian Höfer. Simulating the Emergence of Grammatical Agreement in Multi-agent Language Games. Twenty-Second International Joint Conference on Artificial Intelligence, pp. 61-66, 2011.

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Sebastian Höfer. Anwendungen der Slow Feature Analysis in der humanoiden Robotik. Diploma Thesis, Humboldt University of Berlin, Germany, 2011. Download Bibtex entry


Sebastian Höfer and Manfred Hild and Matthias Kubisch. Using Slow Feature Analysis to Extract Behavioural Manifolds Related to Humanoid Robot Postures. Tenth International Conference on Epigenetic Robotics, pp. 43-50, 2010.

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Sebastian Höfer and Manfred Hild. Using Slow Feature Analysis to Improve the Reactivity of a Humanoid Robot's Sensorimotor Gait Pattern. International Conference on Neural Computation, pp. 212-219, 2010.

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Michael Spranger and Sebastian Höfer and Manfred Hild. Biologically Inspired Posture Recognition and Posture Change Detection for Humanoid Robots. IEEE International Conference on Robotics and Biomimetics, pp. 562-567, 2009.

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Janika Urig
Tel. +49 30 314-73 110
Fax +49 30 314-21 116
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TU Berlin
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