Anthony Tompkins

• PhD Student at The University of Sydney

We establish a general form of explicit, input-dependent, measure-valued warpings for learning nonstationary kernels. While stationary kernels are ubiquitous and simple to use, they struggle to adapt to functions that vary in smoothness with respect to the input. The proposed learning algorithm warps inputs as conditional Gaussian measures that con...

Creating accurate spatial representations that take into account uncertainty is critical for autonomous robots to safely navigate in unstructured environments. Although recent LIDAR based mapping techniques can produce robust occupancy maps, learning the parameters of such models demand considerable computational time, discouraging them from being...

Creating accurate spatial representations that take into account uncertainty is critical for autonomous robots to safely navigate in unstructured environments. Although recent LIDAR based mapping techniques can produce robust occupancy maps, learning the parameters of such models demand considerable computational time, discouraging them from being...

Kernel methods have been successfully used in various domains to model nonlinear patterns. However, the structure of the kernels is typically handcrafted for each dataset based on the experience of the data analyst. In this paper, we present a novel technique to learn kernels that best fit the data. We exploit the measure-theoretic view of a shift-...

In order to deploy robots in previously unseen and unstructured environments, the robots should have the capacity to learn on their own and adapt to the changes in the environments. For instance, in mobile robotics, a robot should be able to learn a map of the environment from data itself without the intervention of a human to tune the parameters o...

Periodicity is often studied in timeseries modelling with autoregressive methods but is less popular in the kernel literature, particularly for higher dimensional problems such as in textures, crystallography, and quantum mechanics. Large datasets often make modelling periodicity untenable for otherwise powerful non-parametric methods like Gaussian...

Gaussian Processes (GPs) provide an extremely powerful mechanism to model a variety of problems but incur an O(N3) complexity in the number of data samples. Common approximation methods rely on what are often termed inducing points but still typically incur an O(NM2) complexity in the data and corresponding inducing points. Using Random Fourier Fea...

Experimental Mars Rover (EMR)-Mawson is a 6 wheeled rover designed and operated on the Mars Yard of the Powerhouse Museum in Sydney, Australia. The Mars Yard terrain is a physical analogue carefully constructed to simulate the surface of Mars. EMR-Mawson is teleoperated by researchers and students from distant locations. There are times that intend...

Accurate localisation is a valuable tool for the validation of many autonomous and teleoperated vehicles in both indoor and outdoor environments. For planetary robotic research platforms such as the Experimental Mars Rover (EMR) in the Powerhouse Museum in Sydney, gathering consistent and accurate pose information is essential for validation of exp...

The Experimental Mars Rover (EMR) at the Mars Yard in the Powerhouse Museum, Sydney, is a battery powered platform being used extensively by experienced engineers and researchers from universities as well as teleoperated by high school students from different states of Australia. In order to maximise the EMRs operational time, occasionally, its bat...