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),...