Learning User Preferences from Corrections on State Lattices
Nils Wilde,Dana Kulić,Stephen L. Smith,Nils Wilde,Dana Kulić,Stephen L. Smith
Enabling a broader range of users to efficiently deploy autonomous mobile robots requires intuitive frameworks for specifying a robot's task and behaviour. We present a novel approach using learning from corrections (LfC), where a user is iteratively presented with a solution to a motion planning problem. Users might have preferences about parts of a robot's environment that are suitable for robot...
