Towards Blended Reactive Planning and Acting using Behavior Trees

TL;DR

This paper presents a method for automatically generating and updating Behavior Trees for robots, combining planning and reactive acting to adapt efficiently to dynamic environments and external disturbances.

Contribution

The paper introduces a novel approach that integrates back-chaining planning with Behavior Trees, enabling automatic creation and real-time updating for reactive robot control.

Findings

Effective in dynamic environments with external disturbances

Reduces need for replanning by reusing existing Behavior Trees

Demonstrated in two robotics scenarios

Abstract

In this paper, we show how a planning algorithm can be used to automatically create and update a Behavior Tree (BT), controlling a robot in a dynamic environment. The planning part of the algorithm is based on the idea of back chaining. Starting from a goal condition we iteratively select actions to achieve that goal, and if those actions have unmet preconditions, they are extended with actions to achieve them in the same way. The fact that BTs are inherently modular and reactive makes the proposed solution blend acting and planning in a way that enables the robot to efficiently react to external disturbances. If an external agent undoes an action the robot reexecutes it without re-planning, and if an external agent helps the robot, it skips the corresponding actions, again without replanning. We illustrate our approach in two different robotics scenarios.