Rowan McAllister

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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)
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)
— Model-based reinforcement learning (RL) allows an agent to discover good policies with a small number of trials by generalising observed transitions. Data efficiency can be further improved with a probabilistic model of the agent's ignorance about the world, allowing it to choose actions under uncertainty. Bayesian modelling offers tools for this task,(More)
— This paper presents an approach for a reconfig-urable multi-modal mobile robot operating in an indoor environment , based on a probabilistic framework. The modalities are composed of a path planning method, a reactive motion strategy, and an emergency stop. While the mobile robot is achieving its mission to reach its goal, a Hidden Markov Model is used to(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)
— This paper proposes an approach to achieve resilient navigation in the context of indoor mobile robots. Resilient navigation seeks to mitigate the impact of control, localisation, or map errors on the safety of the platform while enforcing the robot's ability to achieve its goal. We show that resilience to unpredictable errors can be achieved by combining(More)
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