Neural Control

Inspired by our event driven perception and quick sparsity-aware neural hardware and observations like seeing a bat catching an insect and an octopus manoeuvring its eight arms, we have been working to close the gap between the control we observe in nature and robotic applications. We use machine learning tools to find the optimal control quickly and with limited power budget. We are interested in learning system models and studying how to incorporate continual learning for robust adaptive control strategies. The algorithms are tested on systems in simulation and on physical robots. We perform physical experiments on our high-performance “INIvincible” F1TENTH race car (in collaboration with the ForzaETH team in PBL at ETH) and our physical cartpole robots. We aim in proving the usefulness of our ideas in the competitive setting of F1TENTH international autonomous race.  Since 2018, we organized a series of neural control topic areas at the Telluride Neuromorphic Workshops.

Questions we are working to answer include:


Car in F1TENTH simulator running our variation of Model Predictive Path Integral control. Notice drifting in the curves.
Our cartpole performing first successful swing-ups programmed by Jerome Jeannin (MSc D-ITET)
Florian Bolli testing our toy car just constructed by Constantin Koch (BSc D-PHYS).
Frederik Heetmeyer (MSc D-ITET) and Ante Marić (University of Zagreb) developing control algorithms and testing them on cart

Neural Control Publications

Supervised undergraduate projects in Neural Control

To gain experience in neural control we supervised a large number of ETH student projects, starting in 2021

Concluded NC projects