Reinforcement learning for autonomous robot navigation

The goal of the Boticelli project is to show the usefulness of piecewise linear functions (PLFs) in various tasks of autonomous mobile robot navigation. One of the tasks is to deal with the world model where the 3D occupancy function is efficiently represented as a PLF; and the other is to represent the value function during reinforcement learning for the purpose of path planning. The paper overviews the project and demonstrates that the PLF approximation, as a solution to Bellman´s equation, can support robot motion planning