Nick Hawes Seminar (1)

Mission planning for long-horizon tasks requires the planning agent to use a model to encode its interaction with its environment. In most robotic tasks some parts of this model are known with certainty, whereas other parts may only be known with uncertainty at design time, and must be updated via learning either between missions (i.e. “offline") or during execution (“online"). In this talk I’ll give a high-level summary of our recent work on mission planning with such uncertain models. This will range from planning in MDPs with a Gaussian Process prior over a single state features,  to planning in Uncertain MDPs and Bayes-Adaptive MDPs where the true model cannot be known with certainty.