We focus on machine learning, mostly deep learning and recurrent neural networks, and its application to movement representation, robotics, and sensor integration.

We use and further develop deep learning, deep recurrent neural networks, convolutional neural networks, autoencoders, etc. and use them in various data related to movement, robotics, and sensory processing.

Using deep learning, we do end-to-end learning on visual and tactile data.  We also develop methods for anomaly detection.

Using our machine-learning methodologies, we model movement in latent domains and use those in movement prediction, analysis, and control.  These models are combined with biomechanical and motor control models.

Limb rehabilitation and assistive robotics are paramount applications of the techniques developed in biomimetic robotics. Using our machine-learning methods, we focus upon human-computer interfaces to aid the disabled regain the lost limb functionality. In our view, both rehabilitation and prosthetics rely on re-establishing the sensori-motor loop with the missing limb.



In this work a model for ultrasonic motors should be developed and a robust controller designed. The stability/robustness of the controller should be analysed. The motor parameters are to be identified and the control scheme should be implemented and evaluated on a 1-DoF testbed.

You are asked to work on real robotic data to understand control parameters of a light-weight robotic arm. 

Why are robotic hands so clumsy?  In an attempt to solve this problem, we investigate the use of tactile sensors for robotic in-hand manipulation. In particular, based on a number of tactile sensors (the BioTac; the DLR tactile sensor; and the iCub tactile sensor), we want to create algorithms that can use such sensing to manipulate objects. In cleartext: write, unscrew a bulb, or hand over a knife with a robotic hand.

Picture of  Axel von Arnim

Axel von Arnim

fortiss: software architect
Human Brain Project - Neurorobotics
Picture of  Bastien Achard

Bastien Achard

TUM: MSc candidate
cNNs for object localisation
Picture of  Benedikt Staffler

Benedikt Staffler

MPI/TUM: PhD candidate
cNN for connectome
Picture of  Christian Osendorfer

Christian Osendorfer

TUM: PhD candidate
unsupervised learning, deep networks
osendorfin.tumde
Picture of  Daniela Korhammer

Daniela Korhammer

TUM: PhD candidate
ML-based movement models
korhammdin.tumde
Picture of  Frederik Diehl

Frederik Diehl

TUM: student assistant
tactile force learning
Picture of  Georg Stillfried

Georg Stillfried

DLR: PhD candidate
kinematics of the human hand
georg.stillfrieddlrde
Picture of  Hannes Höppner

Hannes Höppner

DLR: PhD candidate
human impedance
hannes.hoeppnerdlrde, +49 8153 28-1062
Picture of  Holger Urbanek

Holger Urbanek

DLR: PhD candidate
EMG conditioning
holger.urbanekdlrde, +49 8153 28-2450
Picture of  Jörn Vogel

Jörn Vogel

DLR: PhD candidate
BCI robot control
joern.vogeldlrde, +49 8153 28-2166
Picture of  Justin Bayer

Justin Bayer

TUM: PhD candidate
time series learning
bayer.justingooglemailcom
Picture of  Lucia Seitz

Lucia Seitz

TUM: MSc candidate
neural data regression
lucia.seitztumde
Picture of  Mark Hartenstein

Mark Hartenstein

TUM: MSc candidate
tactile sensing
Picture of  Markus Kühne

Markus Kühne

TUM: PhD candidate
MR-compatible haptic interfaces
markus.kuehnetumde
Picture of  Marvin Ludersdorfer

Marvin Ludersdorfer

fortiss: PhD candidate
anomaly detection
ludersdorferfortissorg
Picture of  Maximilian Karl

Maximilian Karl

TUM: PhD candidate
efficient inference
Picture of  Maximilian Soelch

Maximilian Soelch

TUM: MSc candidate
DL for anomaly detection
Picture of  Michael Strohmayr

Michael Strohmayr

DLR: postdoc
the DLR artificial skin
michael.strohmayrdlrde
Picture of  Nutan Chen

Nutan Chen

TUM: PhD candidate
hand modelling
nutanin.tumde
Picture of  Patrick van der Smagt

Patrick van der Smagt

Director of BRML labs
fortiss, An-Institut der Technischen Universität München
Professor for Biomimetic Robotics and Machine Learning, TUM
Chairman of Assistenzrobotik e.V.
smagtbrmlorg, +49 89 289-25793
Picture of  Rachel Hornung

Rachel Hornung

DLR: PhD candidate
rehabilitation robotics
rachel.hornungdlrde
Picture of  Sebastian Urban

Sebastian Urban

TUM: PhD candidate
learning skin data
surbantumde, +49 89 289-25794
Picture of  Thomas Rückstiess

Thomas Rückstiess

TUM: PhD candidate
reinforcement learning and design
rueckstiin.tumde
Picture of  Wiebke Koepp

Wiebke Koepp

TUM: MSc candidate
novel activation functions in neural networks



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Below our 15 most recent publications. If you need more, follow the link. And note: All downloadable PDFs are for personal use only. Please do not redistribute.

Vogel J, Haddadin S, Jarosiewicz B, Simeral JD, Bacher D, Hochberg MM, Donoghue JP, Smagt P van der (2015). An assistive decision-and-control architecture for force-sensitive hand--arm systems driven by human--machine interfaces. The International Journal of Robotics Research (IJRR).
Urban S, Ludersdorfer M, Smagt P van der (2015). Sensor Calibration and Hysteresis Compensation with Heteroscedastic Gaussian Processes. IEEE Sensors.
Milacskí ZA, Ludersdorfer M, Smagt P van der, Lörincz A (2015). Robust Detection of Anomalies via Sparse Methods. Proc. ICONIP
Chen N, Urban S, Bayer J, Smagt P van der (2015). Measuring Fingertip Forces from Camera Images for Random Finger Poses. Proc. IEEE International Conference on Robotics and System (IROS)
Höppner H, Grebenstein M, Smagt P van der (2015). Two-dimensional orthoglide mechanism for revealing areflexive human arm mechanical properties. Proc. IEEE International Conference on Intelligent Robots and Systems (IROS 2015)
Urban S, Smagt P van der (2015). A Neural Transfer Function for a Smooth and Differentiable Transition Between Additive and Multiplicative Interactions. arXiv
Fischer P, Dosovitskiy A, Ilg E, Haeusser P, Hazirbas C, Golkov V, Smagt P van der, Cremers D, Brox T (2015). FlowNet: Learning Optical Flow with Convolutional Networks. Proc. ICCV
Bayer J, Osendorfer C (2014). Learning Stochastic Recurrent Networks. Workshop on Advances in Variational Inference, Neural Information Processing Systems 2014
Hornung R, Urbanek H, Klodmann J, Osendorfer C, Smagt P van der (2014). Model-free robot anomaly detection. Proc. IEEE/RSJ International Conference on Robotics and Systems (IROS)
Bayer J, Osendorfer C, Korhammer D, Chen N, Urban S, Smagt P van der (2014). On Fast Dropout and its Applicability to Recurrent Networks. Proceedings of the International Conference on Learning Representations
Ergin MA, Kühne M, Thielscher A, Peer A (2014). Design of a New MR-compatible Haptic Interface with Six Actuated Degrees of Freedom. IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob)
Osendorfer C, Soyer H, Smagt P van der (2014). Image Super-Resolution with Fast Approximate Convolutional Sparse Coding. Neural Information Processing 8836
Chen N, Urban S, Osendorfer C, Bayer J, Smagt P van der (2014). Estimating finger grip force from an image of the hand using Convolutional Neural Networks and Gaussian Processes. Robotics and Automation (ICRA), 2014 IEEE International Conference on
Höppner H, Wiedmeyer W, Smagt P van der (2014). A new biarticular joint mechanism to extend stiffness ranges. Robotics and Automation (ICRA), 2014 IEEE International Conference on
Stillfried G, Hillenbrand U, Settles M, Smagt P van der (2014). MRI-based skeletal hand movement model. In Ravi Balasubramanian and Veronica J. Santos (Eds.) The Human Hand as an Inspiration for Robot Hand Development 95 49-75.