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.
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.