Your nail tells you how firmly you are gripping. We use this method to get accurate representations of grip force. But our approach has its limits. We need you to improve this methods and make a difference in science. A working device and a paper or two in a scientific journal or international conference will be your output.
In biomechanics, we create details models of human kinematic and dynamic properties of arms, hands, fingers, and legs. These models are needed to understand which properties of human movement are intrinsic---caused by muscles, tendons, ligaments and bones---and which are controlled by the nervous system. Our resulting models are used in the construction and control of novel robotic systems, including prosthetic hands and robotic arms and legs.
The use of surface electromyography (sEMG) for prosthetic control has been in place since the 1960's. We go a step further. On the one hand, we optimise the conditioning of the sEMG signal, and find new ways of relating it to limb movement. But we also look at different channels to control prosthetic and assistive robotic devices, including central nervous system implants.
In Machine Learning, we investigate methods to map high-dimensional non-linear data within a control process. Even though most of our data are related to the above fields of research, the methods we employ and develop are general methods, in which we combine deep belief networks with time sequence learning.
Limb rehabilitation and prosthetics are paramount applications of the techniques developed in biomimetic robotics. 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. This includes both ways: feed-forward control by detecting the patient’s will to move and sensorial feedback by transducing digital readings to feelings.
Justin BayerTUM: PhD candidate
time series learning
Bojan KolosnjajiDLR: MSc candidate
learning hand models
Claudio CastelliniDLR: postdoc
prosthetics and rehabilitation
claudio.castellinidlrde, +49 8153 28-1093
Hannes HöppnerDLR: PhD candidate
hannes.hoeppnerdlrde, +49 8153 28-1062
Rachel HornungDLR: PhD candidate
Daniela KorhammerTUM: MSc candidate
Dominic LakatosDLR: PhD candidate
human arm dynamics
dominic.lakatosdlrde, +49 8153 28-2467
Marvin LudersdorferTUM: student
Dominik MautzTUM: BSc candidate
Nutan ChenTUM: PhD candidate
Christian OsendorferTUM: PhD candidate
unsupervised learning, deep networks
Thomas RückstiessTUM: PhD candidate
reinforcement learning and design
Alexander SchiendorferTUM: MSc candidate
Patrick van der SmagtTUM: Director of BRML labs
smagtbrmlorg, +49 89 289-25793
Hubert SoyerTUM: MSc candidate
deep convolutional networks
Georg StillfriedDLR: PhD candidate
kinematics of the human hand
Michael StrohmayrDLR: postdoc
the DLR artificial skin
Sebastian UrbanTUM: PhD candidate
learning skin data
surbantumde, +49 89 289-25787
Holger UrbanekDLR: PhD candidate
holger.urbanekdlrde, +49 8153 28-2450
Jörn VogelDLR: PhD candidate
BCI robot control
joern.vogeldlrde, +49 8153 28-2166
Wolfgang WiedmeyerDLR: student
wolfgang.wiedmeyerdlrde, +49 8153 28-1056
Julian ZafirisTUM: MSc candidate
Bayesian nonparametric regression
Stefan ZoellTUM: design
Your name could be herewant to join our team? check out the positions on the left.
- Machine Learning lecture at TUM
- DLR Bionics group website
- DLR Institute of Robotics and Mechatronics website
- TUM chair of Robotics and Embedded Systems
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.