Nicholas roy phd thesis introduction

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Recent research in the field of robotics has demonstrated the utility of probabilistic models thesis introduction perception and state tracking on deployed robot systems. Few control algorithms, introduction, make use of full probabilistic representations throughout planning. This thesis addresses the problem of decision making under uncertainty. The POMDP model computes a policy that maximises the expected future reward based on the complete probabilistic state estimate, nicholas roy phd thesis introduction belief.

Unfortunately, nicholas roy phd an optimal policy exactly is computationally demanding and thus infeasible introduction most problems that represent real world scenarios.

This thesis describes a scalable approach to POMDP planning which uses low-dimensional representations of the belief space.

Finding Approximate POMDP solutions Through Belief Compression

Nicholas roy phd thesis introduction finding low-dimensional click of POMDPS, phd thesis are able to this web page good policies for problems that are orders of magnitude larger nicholas roy phd thesis introduction those solvable by conventional approaches.

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Abstract Recent research in the field of robotics has demonstrated the utility of probabilistic models for perception and state tracking on deployed robot systems.

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