Learning Metareasoning Policies for Motion Planning

Abstract

Metareasoning is the process of reasoning about reasoning. This thesis applies metareasoning to motion planning and evaluates three different metareasoning policies. Two policies are rule-based policies and are human specified. The third policy is a smart metareasoning policy that learns from the robot's past experiences, particularly the front camera images. The data is obtained by running the robot without a metareasoner in modular test scenarios which can be combined to form multiple real-world situations. The policy is stored in the form of the weights of a neural network. The neural network-based model used for this research is a multi-input classifier that chooses an optimal planner combination from amongst eight different planner combinations. The metareasoners are tested on a Unity simulator with a Clearpath Warthog ground robot. This thesis tests the performance of the robot under eight different test scenarios for eight different planner combinations and shows an improvement in the robot's success rate when using a metareasoner. Lastly, this thesis also provides a comparative study between a rule-based metareasoner and a smart metareasoner by introducing two new test scenarios which are not part of the robot's past experiences.