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Research within SLMC

Research Interests

The SLMC group has a broad and highly inter-disciplinary research agenda spanning statistical machine learning, formal learning theory, learning in connectionist systems (artificial and biological); robotic, humanoid and biological motor control, adaptive/learning control; and multimodal cue integration and attentional strategies.


Some of our work has resulted in software packages that may be useful to other researchers or practitioners:
  • LWPR

    Locally Weighted Projection Regression (LWPR) is an algorithm developed in our group that achieves nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. We developed a C-library with wrappers for C++, Matlab/Octave, and Python.
  • EXOTica

    The EXOTica library is a generic Optimisation Toolset for Robotics platforms, written in C++. Its motivation is to provide a more streamlined process for developing algorithms for such tasks as Inverse-Kinematics and Trajectory Optimisation.

Undergraduate and MSc Projects

Some of our recent projects for undergraduate and MSc students can be found here.


An up-to-date publication list can be found here.

Some of the topics of recent interest are listed below (with links to papers).

  • Statistical Learning
    • Nonparametric Learning in High Dimensions (papers)
    • Bayesian Approaches (papers)
    • Kernel and Exact Incremental methods- Functional Analysis (papers)
    • Active/Query based learning & Learning curves (papers)
    • Reinforcement Learning (papers)
  • Visual Attention & Oculomotor Control (papers)
  • Real Time Learning for Robot Control & High Dimensional Systems (papers)
  • Dimensionality Reduction (papers)
  • Multimodal Learning and Cue Integration
  • Haptic Discrimination: Encoding and Decoding Strategies
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