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When robots are deployed in the field for environmental monitoring theytypically execute pre-programmed motions, such as lawnmower paths, instead ofadaptive methods, such as informative path planning. One reason for this isthat adaptive methods are dependent on parameter choices that are both criticalto set correctly and difficult for the non-specialist to choose. Here, we showhow to automatically configure a planner for informative path planning bytraining a reinforcement learning agent to select planner parameters at eachiteration of informative path planning. We demonstrate our method with 37instances of 3 distinct environments, and compare it against pure (end-to-end)reinforcement learning techniques, as well as approaches that do not use alearned model to change the planner parameters. Our method shows a 9.53% meanimprovement in the cumulative reward across diverse environments when comparedto end-to-end learning based methods; we also demonstrate via a fieldexperiment how it can be readily used to facilitate high performance deploymentof an information gathering robot.

Learned Parameter Selection for Robotic Information Gathering