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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++ and with bindings for Python. Its motivation is to provide a more streamlined process for developing and benchmarking algorithms for motion planning and control.

UoE-AIRS Joint Project 

The University of Edinburgh (UoE) and Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) collaboration project. 

Project details and open positions can be found here.

Undergraduate and MSc Projects

Some of our 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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