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.
We'd like you to devise and consolidate a system which robustly predicts robot and human arm movement based on visual data. You will be working with variational autoencoders, recurrent neural networks, and convolutional neural networks in a lab which focuses on machine learning, deep learning, and robotics.
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.
Axel von Arnimfortiss: software architect
Human Brain Project - Neurorobotics
Bastien AchardTUM: MSc candidate
cNNs for object localisation
Benedikt StafflerMPI/TUM: PhD candidate
cNN for connectome
Christian OsendorferTUM: PhD candidate
unsupervised learning, deep networks
Christopher WolfTUM: MSc candidate
Variational Hamiltonian Monte Carlo
Daniela KorhammerTUM: PhD candidate
ML-based movement models
Frederik DiehlTUM: student assistant
tactile force learning
Grady JensenLMU: Ph.D. candidate
Hannes HöppnerDLR: PhD candidate
hannes.hoeppnerdlrde, +49 8153 28-1062
Holger UrbanekDLR: PhD candidate
holger.urbanekdlrde, +49 8153 28-2450
Jörn VogelDLR: PhD candidate
BCI robot control
joern.vogeldlrde, +49 8153 28-2166
Justin BayerTUM: PhD candidate
time series learning
Lucia SeitzTUM: MSc candidate
neural data regression
Markus KühneTUM: PhD candidate
MR-compatible haptic interfaces
Marvin Ludersdorferfortiss: PhD candidate
Maximilian KarlTUM: PhD candidate
Maximilian SoelchTUM: PhD candidate
robot control with deep learning
Nutan ChenTUM: PhD candidate
Patrick van der SmagtDirector of BRML labs
fortiss, An-Institut der Technischen Universität München
Professor for Biomimetic Robotics and Machine Learning, TUM
Chairman of Assistenzrobotik e.V.
Rachel HornungDLR: PhD candidate
Sebastian UrbanTUM: PhD candidate
learning skin data
surbantumde, +49 89 289-25794
Wiebke KoeppTUM: MSc candidate
novel activation functions in neural networks
Your name could be herewant to join our team? check out the positions on the left.
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.