A survey of Behavior Trees in robotics and AI

Iovino, Matteo; Scukins, Edvards; Styrud, Jonathan; Ögren, Petter; Smith, Christian

doi:10.1016/j.robot.2022.104096

Cited by 114 publications

(48 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nowadays, many of the main environments dedicated to videogame programming integrate BTs ( Unity, 2021 ; Unreal Engine, 2022 ), offering also visual interfaces for designing and automatically implementing them. Their usefulness has been rapidly acknowledged by researchers and developers of AI for robotics ( Marzinotto et al, 2014 ; Ghzouli et al, 2020 ; Iovino et al, 2020 ), also recognizing their effectiveness in generalizing several other formalisms for coding hybrid dynamical systems ( Colledanchise and Ögren, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

Generalized Behavior Framework for Mobile Robots Teaming With Humans in Harsh Environments

2022

View full text Add to dashboard Cite

Industrial contexts, typically characterized by highly unstructured environments, where task sequences are difficult to hard-code and unforeseen events occur daily (e.g., oil and gas, energy generation, aeronautics) cannot completely rely upon automation to substitute the human dexterity and judgment skills. Robots operating in these conditions have the common requirement of being able to deploy appropriate behaviours in highly dynamic and unpredictable environments, while aiming to achieve a more natural human-robot interaction and a broad range of acceptability in providing useful and efficient services. The goal of this paper is to introduce a deliberative framework able to acquire, reuse and instantiate a collection of behaviours that promote an extension of the autonomy periods of mobile robotic platforms, with a focus on maintenance, repairing and overhaul applications. Behavior trees are employed to design the robotic system’s high-level deliberative intelligence, which integrates: social behaviors, aiming to capture the human’s emotional state and intention; the ability to either perform or support various process tasks; seamless planning and execution of human-robot shared work plans. In particular, the modularity, reactiveness and deliberation capacity that characterize the behaviour tree formalism are leveraged to interpret the human’s health and cognitive load for supporting her/him, and to complete a shared mission by collaboration or complete take-over. By enabling mobile robotic platforms to take-over risky jobs which the human cannot, should not or do not want to perform the proposed framework bears high potential to significantly improve the safety, productivity and efficiency in harsh working environments.

show abstract

Section: Methodsmentioning

confidence: 99%

Generalized Behavior Framework for Mobile Robots Teaming With Humans in Harsh Environments

2022

View full text Add to dashboard Cite

show abstract

“…Our parametric skills can utilize the world model to retrieve knowledge and are implemented with BTs. A BT [28], is a plan representation and execution tool that is used in many areas including computer games and robotics [28], [29]. As in [30], [31], we define it as a directed acyclic graph with nodes and edges.…”

Section: A Skill Representationmentioning

confidence: 99%

Learning Skill-based Industrial Robot Tasks with User Priors

Mayr¹,

Hvarfner²,

Chatzilygeroudis³

et al. 2022

Preprint

View full text Add to dashboard Cite

Robot skills systems are meant to reduce robot setup time for new manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often difficult to find the right skill parameters. One strategy is to learn these parameters by allowing the robot system to learn directly on the task. For a learning problem, a robot operator can typically specify the type and range of values of the parameters. Nevertheless, given their prior experience, robot operators should be able to help the learning process further by providing educated guesses about where in the parameter space potential optimal solutions could be found. Interestingly, such prior knowledge is not exploited in current robot learning frameworks. We introduce an approach that combines user priors and Bayesian optimization to allow fast optimization of robot industrial tasks at robot deployment time. We evaluate our method on three tasks that are learned in simulation as well as on two tasks that are learned directly on a real robot system. Additionally, we transfer knowledge from the corresponding simulation tasks by automatically constructing priors from well-performing configurations for learning on the real system. To handle potentially contradicting task objectives, the tasks are modeled as multi-objective problems. Our results show that operator priors, both user-specified and transferred, vastly accelerate the discovery of rich Pareto fronts, and typically produce final performance far superior to proposed baselines.

show abstract

“…ese properties are crucial in many applications, which have led to the spread of BTs from computer game programming to many branches of AI and Robotics [6]. Several authors have already shown that Behavior Trees are a promising approach to model autonomous behaviour in dynamic environments [6][7][8][9][10][11], [25,27].…”

Section: Introduction: Need For Embodied Intelligencementioning

confidence: 99%

“…A growing amount of literature discusses fundamental aspects and applications of behavior trees for robotics [3,6,7]. Several authors provide formal de nitions of Behavior Trees [3,4] or propose extensions [12].…”

Section: Introduction: Need For Embodied Intelligencementioning

confidence: 99%

Behavior Trees for Smart Robots Practical Guidelines for Robot Software Development

Dortmans

Punter

2022

Journal of Robotics

View full text Add to dashboard Cite

Behavior Trees are a promising approach to model the autonomous behaviour of robots in dynamic environments. Behavior Trees represent action selection decisions as a tree of decision nodes. The hierarchy of these decision nodes provides the planning of actions of the robot including its reactions on exceptions. Behavior Trees enable flexible planning and replanning of robot behavior while supporting better maintainable decision-making than traditional Finite State Machines. This paper presents an overview of lessons, which we have learned when applying Behavior Trees to various autonomous robots. We present these lessons as a sequence of steps that is meant to support robot software practitioners to develop their systems.

show abstract

A survey of Behavior Trees in robotics and AI

Cited by 114 publications

References 37 publications

Generalized Behavior Framework for Mobile Robots Teaming With Humans in Harsh Environments

Generalized Behavior Framework for Mobile Robots Teaming With Humans in Harsh Environments

Learning Skill-based Industrial Robot Tasks with User Priors

Behavior Trees for Smart Robots Practical Guidelines for Robot Software Development

Contact Info

Product

Resources

About