A Survey of Behavior Trees in Robotics and AI (2005.05842v2)

Abstract: Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade. With rising demands on agent AI complexity, game programmers found that the Finite State Machines (FSM) that they used scaled poorly and were difficult to extend, adapt and reuse. In BTs, the state transition logic is not dispersed across the individual states, but organized in a hierarchical tree structure, with the states as leaves. This has a significant effect on modularity, which in turn simplifies both synthesis and analysis by humans and algorithms alike. These advantages are needed not only in game AI design, but also in robotics, as is evident from the research being done. In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications. The existing literature is described and categorized based on methods, application areas and contributions, and the paper is concluded with a list of open research challenges.

A Survey of Behavior Trees in Robotics and AI

The paper "A Survey of Behavior Trees in Robotics and AI" by Iovino et al. provides a comprehensive review of the application and theoretical developments of Behavior Trees (BTs) as a versatile tool in artificial intelligence, particularly within the domains of robotics and gaming. Originally designed for modular AI in computer games, BTs have increasingly found applications in the robotics landscape due to their hierarchical structure and clear coding interface, which provide advantages in terms of modularity and reactivity over traditional structures like Finite State Machines (FSMs).

The survey begins by examining the foundation and core principles of BTs. BTs structure state transition logic in a hierarchical tree where the nodes denote control flows and execution. This results in a significant improvement in modularity, wherein various submodules (or subtrees) can be rearranged, reused, or modified independently. Key features of BTs include sequences, fallbacks, and parallel nodes, which greatly enhance their capabilities in handling dynamic changes and interruptions in real-time scenarios.

The paper reviews the historical development of BTs, crediting their initial conception to AI programmers in the gaming industry, highlighting their advantages over FSMs in scaling with increased complexity. In gaming, BTs have simplified the design and implementation of NPC behaviors in genres ranging from platformers to real-time strategy games. The paper catalogs applications in major game categories, documenting not only their usability but also the enhancements provided by integrating BTs with machine learning techniques in NPC behavior adaptation.

The survey also outlines the transition of BTs into robotics, discussing their implementation from manipulators to mobile robots and UAVs. In industrial settings, BTs improve the reactivity of robotic arms and collaborative systems, such as the ABB YuMi and other platforms. The versatility and clarity of BTs allow for real-time adjustments and optimizations in response to environmental inputs during complex task sequences like path navigation and object manipulation.

An important aspect of the survey is the exploration of methodologies for automating BT design. It details approaches using Reinforcement Learning and evolutionary algorithms, emphasizing the modular nature of BTs that suits them well for such tasks. The report also addresses manual and hybrid designs, combining traditional planning algorithms with BT structures to achieve robust and flexible solutions.

Significant challenges remain in the integration of learning algorithms with BTs, which the paper identifies as pivotal for future developments in AI. The adaptability of BTs to evolving environments and their role in explanation-friendly AI present both opportunities and obstacles to address in upcoming research. Furthermore, the paper speculates on the potential for BTs to contribute to better explainability, human-robot interaction, and safety in autonomous systems.

In conclusion, this survey highlights BTs' transformation from a game AI tool to a robust methodology for robotics and AI applications. The modularity, adaptability, and structured clarity of BTs contribute significantly to their effectiveness, making them a promising avenue for ongoing and future AI research, particularly in complex, dynamic environments. This paper serves as a fundamental resource for researchers looking to explore or expand upon the application of BTs in both theoretical and practical settings.