Our work focusses on building robustly autonomous robots and other cyber-physical systems, capable of intelligently working with humans and other agents, in dynamic and continually changing environments.

The types of applications we target include:

  • Human-robot collaboration in domains such as customisable manufacturing and personal robotics. A typical use case is where the robot assists a human domain expert, whose own understanding of the domain is not only incredibly rich, but also hard to formalise in ways that could be easily programmed into the robot. So, our challenge is to make the robot capable enough at learning and interacting, so that it can acquire models as it goes and learn to use them to act with increasing levels of skill.
  • Active sensing and predictive modelling platforms, applied within energy and environmental systems. A representative use case might involve the use of a mobile robot or team of robots acting as a sensor network, coupled with algorithms that actively learn models of an underlying physical process (e.g., wind flows) and other dynamics (e.g., demand patterns) in order to enable real-time decisions and process optimisation.
  • This requires development and creative use of methods for:

    • Compositional and incremental methods for model learning and learning to act in multi-scale, dynamic environments. Significant contributions along these lines have included:
      • Use of computational topology and geometric methods for unsupervised learning and clustering, using data as varied as joint level trajectories to optimal policies of Markov Decision Processes.
      • Rapid transfer and policy reuse for decision making in changing environments, through Bayesian choice and online learning over a space of policy fragments.
    • Mechanisms of extended interaction for model selection, structure learning and coordinated action in the face of unknown unknowns. Significant contributions in this area include:
      • Type-based learning methods for multi-agent interaction with little or no prior coordination. We have developed a new model (Harsanyi-Bellman Ad Hoc Coordination) that brings together the notions of Bayesian Nash equilibrium and Bellman optimality, to provide optimal actions from beliefs over a set of "policy types".
      • Prediction and hypothesis testing methods for interactive decision making situations.

    The following 1-minute video, prepared for the University's Research in a Nutshell project, broadly outlines our research agenda and perspectives. See also the associated description:


    Major funding for our research work has come from the following sources (grants are listed in reverse chronological order):