Efficient Planning Under Uncertainty with Incremental Refinement

Online planning under uncertainty on robots and similar agents has very strict performance requirements in order to achieve reasonable behavior in complex domains with limited resources. The underlying process of decision-making and information gathering is correctly modeled by POMDP’s, but their complexity makes many interesting and challenging problems virtually intractable. We address this issue by introducing a method to estimate relevance values for elements of a planning domain, that allow an agent to focus on promising features. This approach reduces the effective dimensionality of problems, allowing an agent to plan faster and collect higher rewards. Experimental validation was performed on two challenging POMDP’s that resemble real-world robotic task planning, where it is crucial to interleave planning and acting in an efficient manner.