Rowan McAllister

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— Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is(More)
Reconfiguration allows a self-reconfiguring modular robot to adapt to its environment. The reconfiguration planning problem is one of the key algorithmic challenges in realizing self-reconfiguration. Many existing successful approaches rely on grouping modules together to act as meta-modules. However, we are interested in reconfiguration planning that does(More)
Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is(More)
We present a data-efficient reinforcement learning algorithm resistant to observation noise. Our method extends the highly data-efficient PILCO algorithm (Deisenroth & Rasmussen, 2011) into partially observed Markov decision processes (POMDPs) by considering the filtering process during policy evaluation. PILCO conducts policy search, evaluating each policy(More)
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