Towards Blended Reactive Planning and Acting using Behavior Trees

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.

References in corpus (1)

• Backward-Forward Search for Manipulation Planning

Cited by in corpus (7)

• On the Implementation of Behavior Trees in Robotics
• Behavior Trees in Robot Control Systems
• LLM-BT: Performing Robotic Adaptive Tasks based on Large Language Models and Behavior Trees
• Multi-Modal MPPI and Active Inference for Reactive Task and Motion Planning
• Act, Perceive, and Plan in Belief Space for Robot Localization
• Distributed Behavior Trees for Heterogeneous Robot Teams
• Sobi: An Interactive Social Service Robot for Long-Term Autonomy in Open Environments