Learning Behavior Trees From Demonstration

Kevin French, Shiyu Wu, Tianyang Pan, Zheming Zhou, Odest Chadwicke Jenkins

Abstract: Robotic Learning from Demonstration (LfD) allows anyone, not just experts, to program a robot for an arbitrary task. Many LfD methods focus on low level primitive actions such as manipulator trajectories. Complex multistep task with many primitive actions must be learned from demonstration if LfD is to encompass the full range of task a user may desire. Existing methods represent the high level task in various forms including, finite state machines, decision trees, formal logic, among others. Behavior trees are proposed as an alternative representation of high level task. Behavior trees are an execution model for the control of a robot designed for real time execution, modularity, and, consequently, transparency. Real time execution allows the robot to reactively perform the task. Modularity allows the reuse of learned primitive actions and high level task in new situations, speeding up the process of learning in new scenarios. Transparency allows users to understand and interactively modify the learned model. Behavior trees are used to represent high level tasks by building on the relationship it has with decision trees. We demonstrate a human teaching our Fetch robot a household cleaning task.