Behavior Tree Learning for Robotic Task Planning through Monte Carlo DAG Search over a Formal Grammar
Emily Scheide,Graeme Best,Geoffrey A. Hollinger,Emily Scheide,Graeme Best,Geoffrey A. Hollinger
We present an algorithm for learning behavior trees for robotic task planning, which alleviates the need for time-intensive or infeasible manual design of control architectures. Our method involves representing the search space of behavior trees as a formal grammar and searching over this grammar by means of a new generalization of Monte Carlo tree search (MCTS) for directed acyclic graphs (DAGs),...
