Marc Deisenroth's Homepage

	Department of Engineering Computational and Biological Learning Lab

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Marc Deisenroth's homepage

I moved to Seattle. My new homepage is here.

Visiting Researcher
Department of Engineering
University of Cambridge, UK

Research Interests

I'm interested in

Bayesian inference and learning
reinforcement learning
Gaussian process models
optimal control
active learning/optimal design
inference and learning in nonlinear dynamical systems
signal processing

News

Re-locating (2010-01-28)

I moved to the University of Washington (Seattle).

PhD defended (2009-12-04)

I just defended my PhD thesis.

Workshop paper accepted (2009-09-02)

Our paper on Efficient Reinforcement Learning for Motor Control got accepted.

NIPS workshop accepted (2009-08-26)

Our NIPS workshop on Probabilistic Approaches for Robotics and Control got accpeted.

ICML paper accepted and online (2009-04-22)

The paper is about Kalman filtering in nonlinear dynamic systems using Gaussian process models. More information and the paper itself are here.

Gaussian Process Dynamic Programming published (2009-03-15)

The original article (requires access to ScienceDirect) is here. A pre-print is freely available online.

ALGPDP code online (2009-01-31)

A preliminary version of the ALGPDP code is online.

Videos of Double Pendulum Controller (2009-01-12)

I put up flash-videos of the learning process.

Double Pendulum Controller (2008-12-16)

Using probabilistic inference, we can learn to swing up and balance a double pendulum attached to a cart (from scratch). Due to appropriate handling of uncertainties in simulation, we only need few interactions with the real system.

Hardware Experiment (2008-11-25)

Our probabilistic algorithm for fast learning also works in practice with a cart-pole swing-up task. Here is a very brief introduction, what's going on, including snapshots of a hardware experiment and the link to the corresponding video.

Probabilistic RL Algorithm (2008-07-15)

We recently developed a probabilistic reinforcement learning algorithm. An intuitive description and a bunch of videos are here. A paper describing the ideas will be published soon in Lecture Notes on Computer Science. An electronic copy is available here.

MPI for Biological Cybernetics, Tübingen, Germany