# Towards Blended Reactive Planning and Acting using Behavior Trees

arXiv: https://arxiv.org/abs/1611.00230 · HTML: https://mlsanity.com/paper/1611.00230/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2016.

## Built on by (most cited first, 21 of 21)

- [AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers](https://mlsanity.com/paper/2306.06531/) (2023)
- [Behavior Trees in Robot Control Systems](https://mlsanity.com/paper/2203.13083/) (2022)
- [Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human Instructions](https://mlsanity.com/paper/2405.07474/) (2024)
- [Code-as-Symbolic-Planner: Foundation Model-Based Robot Planning via Symbolic Code Generation](https://mlsanity.com/paper/2503.01700/) (2025)
- [Combining Planning and Learning of Behavior Trees for Robotic Assembly](https://mlsanity.com/paper/2103.09036/) (2021)
- [Task and Motion Planning for Execution in the Real](https://mlsanity.com/paper/2406.03641/) (2024)
- [A Study on Training and Developing Large Language Models for Behavior Tree Generation](https://mlsanity.com/paper/2401.08089/) (2024)
- [HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning](https://mlsanity.com/paper/2406.00965/) (2024)
- [A Unified Framework for Real-Time Failure Handling in Robotics Using Vision-Language Models, Reactive Planner and Behavior Trees](https://mlsanity.com/paper/2503.15202/) (2025)
- [Adaptive Manipulation using Behavior Trees](https://mlsanity.com/paper/2406.14634/) (2024)
- [MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration](https://mlsanity.com/paper/2502.18072/) (2025)
- [From Prompts to Pavement: LMMs-based Agentic Behavior-Tree Generation Framework for Autonomous Vehicles](https://mlsanity.com/paper/2601.12358/) (2026)
- [An Extended Convergence Result for Behaviour Tree Controllers](https://mlsanity.com/paper/2308.08994/) (2023)
- [Meta-Modeling of Assembly Contingencies and Planning for Repair](https://mlsanity.com/paper/2103.07544/) (2021)
- [Behavior Tree-Based Task Planning for Multiple Mobile Robots using a Data Distribution Service](https://mlsanity.com/paper/2201.10918/) (2022)
- [Interpretable Responsibility Sharing as a Heuristic for Task and Motion Planning](https://mlsanity.com/paper/2409.05586/) (2024)
- [Acting and Planning with Hierarchical Operational Models on a Mobile Robot: A Study with RAE+UPOM](https://mlsanity.com/paper/2507.11345/) (2025)
- [Advancing Shared and Multi-Agent Autonomy in Underwater Missions: Integrating Knowledge Graphs and Retrieval-Augmented Generation](https://mlsanity.com/paper/2507.20370/) (2025)
- [Progress Constraints for Reinforcement Learning in Behavior Trees](https://mlsanity.com/paper/2602.06525/) (2026)
- [Contract-Grounded Behavior Tree Synthesis via Coding Agents](https://mlsanity.com/paper/2607.12220/) (2026)
- [APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction](https://mlsanity.com/paper/2610.18523/) (2026)

## Abstract (arXiv)

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.