Carl Edward Rasmussen

About

Carl Edward Rasmussen is a leading researcher at the intersection of machine learning and robotics, renowned for his pioneering work on Gaussian Processes (GPs) and their application to data-efficient learning and control. His most influential contribution, "Gaussian Processes for Data-Efficient Learning in Robotics and Control" (2013, 656 citations), established a foundational framework for applying probabilistic machine learning to autonomous reinforcement learning, dramatically reducing the data requirements that have historically constrained real-world robotic systems. This work underpins a broader research vision: enabling affordable, adaptive robots that learn complex skills from limited experience, as demonstrated in his acclaimed studies on low-cost manipulator control (2011, 216 citations). Rasmussen has also advanced the theoretical frontiers of GP methodology itself, notably through his work on Manifold Gaussian Processes (2016, 214 citations), which extends GP flexibility to model complex, non-differentiable functions by learning problem-specific representations. His contributions to robust Bayesian filtering and smoothing further demonstrate his commitment to principled uncertainty quantification in dynamic systems. Across a body of work spanning control theory, Bayesian nonparametrics, and robotics, Rasmussen has profoundly shaped how the machine learning community approaches sample-efficient, uncertainty-aware autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Gaussian Processes for Data-Efficient Learning in Robotics and Control

   656 citations · 2013
2. 2
   Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning

   216 citations · 2011
3. 3
   Manifold Gaussian Processes for regression

   214 citations · 2016
4. 4
   1 Robust Filtering and Smoothing with Gaussian Processes

   82 citations · 2012
5. 5
   Learning to Control a Low-Cost Manipulator Using Data-Efficient Reinforcement Learning

   54 citations · 2012
6. 6
   Modelling and control of nonlinear systems using Gaussian processes with partial model information

   23 citations · 2012
7. 7
   Policy search for learning robot control using sparse data

   20 citations · 2014
8. 8
   Manifold Gaussian Processes for Regression

   9 citations · 2014
9. 9
   Integrated pre-processing for Bayesian nonlinear system identification with Gaussian processes

   5 citations · 2013

Key Collaborators

Contact & Links