Towards Self-Autonomy Evaluation using Behavior Trees

2021 ◽  
Author(s):  
Khusniddin Fozilov ◽  
Yasuhisa Hasegawa ◽  
Kosuke Sekiyama
Keyword(s):  
Behavior Trees

scholarly journals Building the Foundation of Robot Explanation Generation Using Behavior Trees

2021 ◽  
Vol 10 (3) ◽  
pp. 1-31
Author(s):  
Zhao Han ◽  
Daniel Giger ◽  
Jordan Allspaw ◽  
Michael S. Lee ◽  
Henny Admoni ◽  
...  
Keyword(s):  
Autonomous Robots ◽  
Free Form ◽  
Static Structure ◽  
Simulation Code ◽  
Causal Information ◽  
Robot Behavior ◽  
Behavior Trees ◽  
Robot Task ◽  
Task Specification ◽  
Explanation Generation

As autonomous robots continue to be deployed near people, robots need to be able to explain their actions. In this article, we focus on organizing and representing complex tasks in a way that makes them readily explainable. Many actions consist of sub-actions, each of which may have several sub-actions of their own, and the robot must be able to represent these complex actions before it can explain them. To generate explanations for robot behavior, we propose using Behavior Trees (BTs), which are a powerful and rich tool for robot task specification and execution. However, for BTs to be used for robot explanations, their free-form, static structure must be adapted. In this work, we add structure to previously free-form BTs by framing them as a set of semantic sets {goal, subgoals, steps, actions} and subsequently build explanation generation algorithms that answer questions seeking causal information about robot behavior. We make BTs less static with an algorithm that inserts a subgoal that satisfies all dependencies. We evaluate our BTs for robot explanation generation in two domains: a kitting task to assemble a gearbox, and a taxi simulation. Code for the behavior trees (in XML) and all the algorithms is available at github.com/uml-robotics/robot-explanation-BTs.

scholarly journals An expressiveness hierarchy of Behavior Trees and related architectures

2021 ◽  
pp. 1-1
Author(s):  
Oliver Biggar ◽  
Mohammad Zamani ◽  
Iman Shames
Keyword(s):  
Behavior Trees

A Character Decision-Making System for FINAL FANTASY XV by Combining Behavior Trees and State Machines

2019 ◽  
pp. 339-352
Author(s):  
Youichiro Miyake ◽  
Youji Shirakami ◽  
Kazuya Shimokawa ◽  
Kousuke Namiki ◽  
Tomoki Komatsu ◽  
...  
Keyword(s):  
Decision Making ◽  
State Machines ◽  
Behavior Trees ◽  
Decision Making System

scholarly journals IKBT: Solving Symbolic Inverse Kinematics with Behavior Tree (Extended Abstract)

2020 ◽  
Author(s):  
Dianmu Zhang ◽  
Blake Hannaford
Keyword(s):  
Closed Form ◽  
Inverse Kinematics ◽  
Intelligent System ◽  
Logical Reasoning ◽  
Robot Arm ◽  
Kinematics Analysis ◽  
Closed Form Solutions ◽  
Behavior Tree ◽  
Behavior Trees ◽  
High Level

Inverse kinematics solves the problem of how to control robot arm joints to achieve desired end effector positions, which is critical to any robot arm design and implementations of control algorithms. It is a common misunderstanding that closed-form inverse kinematics analysis is solved. Popular software and algorithms, such as gradient descent or any multi-variant equations solving algorithm, claims solving inverse kinematics but only on the numerical level. While the numerical inverse kinematics solutions are relatively straightforward to obtain, these methods often fail, even when the inverse kinematics solutions exist. Therefore, closed-form inverse kinematics analysis is superior, but there is no generalized automated algorithm. Up till now, the high-level logical reasoning involved in solving closed-form inverse kinematics made it hard to automate, so it's handled by human experts. We developed IKBT, a knowledge-based intelligent system that can mimic human experts' behaviors in solving closed-from inverse kinematics using Behavior Tree. Knowledge and rules used by engineers when solving closed-from inverse kinematics are encoded as actions in Behavior Tree. The order of applying these rules is governed by higher level composite nodes, which resembles the logical reasoning process of engineers. It is also the first time that the dependency of joint variables, an important issue in inverse kinematics analysis, is automatically tracked in graph form. Besides generating closed-form solutions, IKBT also explains its solving strategies in human (engineers) interpretable form. This is a proof-of-concept of using Behavior Trees to solve high-cognitive problems.

Probabilistic Timed Behavior Trees

2007 ◽  
pp. 156-175 ◽  
Author(s):  
Robert Colvin ◽  
Lars Grunske ◽  
Kirsten Winter
Keyword(s):  
Behavior Trees
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