Felix Berkenkamp portrait

I am a research scientist at the Bosch Center for AI near Stuttgart, Germany. Previously I completed my PhD at the Learning & Adaptive Systems Group at ETH Zurich, for which I received the ELLIS PhD award. During my PhD I was advised by Andreas Krause and was co-advised by Angela Schoellig at the University of Toronto.

My goal is to enable dynamic systems to safely and autonomously learn in uncertain real-world environments. This requires new reinforcement learning algorithms that respect the physical limitations and constraints of dynamic systems and provide theoretical safety guarantees.

I held a AI fellowship from the Open Philanthropy Project and was an Associated Fellow at the Max Planck ETH Center for Learning systems. Previously I was the Workflow Co-chair for ICML 2018 and a postgraduate affiliate at the Vector institute. I have completed research internships at Microsoft Research and Deepmind.

Talks and Lectures

Guest lecture on Safe Bayesian Optimization for CS 159: Data-Driven Algorithm Design at Caltech.

ETH Day 2018 short presentation (in German)

Invited talk at the Workshop on Reliable AI 2017

NIPS/CoRL 2017: "Safe Model-based Reinforcement Learning with Stability Guarantees".

CDC 2016: "Safe learning of regions of attraction for nonlinear, uncertain systems with Gaussian processes".

ICRA 2016: "Safe Controller Optimization for Quadrotors with Gaussian Processes".

Research videos

ICRA paper: "Safe Controller Optimization for Quadrotors with Gaussian Processes".

ECC paper: "Safe and robust learning control with Gaussian processes"

NIPS paper: "Safe exploration in finite Markov Decision Processes with Gaussian processes".

ICRA paper: "Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization".

NeurIPS paper: "Safe exploration in finite Markov Decision Processes with Gaussian processes".