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Learning Behavior Trees for Collaborative Robotics
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-6119-6399
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis aims to address the challenge of generating task plans for robots in industry-relevant scenarios. With the increase in small-batch production, companies require robots to be reprogrammed frequently for new tasks. However, maintaining a team of operators with specific programming skills is only cost-efficient for large-scale production. The increase in automation targets companies where humans share their working environment with robots, expanding the scope of manufacturing applications. To achieve that, robots need to be controlled by task plans, which sequence and optimize the execution of actions. This thesis focuses on generating task plans that are reactive, transparent and explainable, modular, and automatically synthesized. These task plans improve the robot’s autonomy, fault-tolerance, and robustness. Furthermore, such task plans facilitate the collaboration with humans, enabling intuitive representations of the plan and the possibility for humans to prompt instructions at run-time to modify the robot’s behavior. Lastly, autonomous generation decreases the programming skills required for the operator to program a robot, and optimizes the task plan. This thesis discusses the use of Behavior Trees (BTs) as policy representations for robotic task plans. It compares the modularity of BTs and Finite State Machines (FSMs) and concludes that BTs are more effective for industrial scenarios. This thesis also explores the automatic and intuitive generation of BTs using Genetic Programming and Learning from Demonstration methods, respectively. The proposed methods aim to time-efficiently evolve BTs for mobile manipulation tasks and allow non-expert users to intuitively teach robots manipulation tasks. This thesis highlights the importance of user experience in task solving and how it can benefit evolutionary algorithms. Finally, it proposes the use of previously learned BTs from demonstration to intervene in the unsupervised learning process.

Abstract [sv]

Den här avhandlingen syftar till att ta itu med utmaningen att generera uppgiftsplaner för robotar i industriella scenarier. Med ökningen av småskalig produktion kräver företag att robotar omprogrammeras frekvent för nya uppgifter. Att upprätthålla en grupp operatörer med specifika programmeringsfärdigheter är dock endast kostnadseffektivt för storskalig produktion. Ökningen av automation riktar sig till företag där människor delar sin arbetsmiljö med robotar och utökar omfattningen av tillverkningsapplikationer. För att uppnå detta måste robotar styras av uppgiftsplaner som sekvenserar och optimerar utförandet av åtgärder. Denna avhandling fokuserar på att generera uppgiftsplaner som är reaktiva, transparenta och förklarbara, modulära och automatiskt syntetiserade. Dessa uppgiftsplaner förbättrar robotens autonomi, feltolerans och robusthet. Dessutom underlättar sådana uppgiftsplaner samarbetet med människor genom att möjliggöra intuitiva representationer av planen och möjligheten för människor att ge instruktioner vid körningstid för att ändra robotens beteende. Slutligen minskar autonom generering programmeringsfärdigheterna som krävs för att operatören ska kunna programmera en robot och optimerar uppgiftsplanen. Denna avhandling diskuterar användningen av beteendeträd (BTs) som policyrepresentationer för robotiska uppgiftsplaner. Den jämför moduleringen av BT och deterministiska tillståndsmaskiner (FSMs) och drar slutsatsen att BTs är mer effektiva för industriella scenarier. Denna avhandling utforskar också den automatiska och intuitiva generationen av BTs med hjälp av genetisk programmering och lärande från demonstrationsmetoder, respektive. De föreslagna metoderna syftar till att tidsmässigt utveckla BTs för mobila manipulationuppgifter och tillåta icke-experter att intuitivt lära robotar manipulationsuppgifter. Denna avhandling belyser vikten av användarupplevelsen i uppgiftslösning och hur den kan gynna evolutionära algoritmer. Slutligen föreslår den användningen av tidigare inlärda BTs från demonstration för att ingripa i den oövervakade inlärningsprocessen.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2023. , p. xi, 127
Series
TRITA-EECS-AVL ; 2023:46
Keywords [en]
Collaborative Robotics, Behavior Trees
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-327210ISBN: 978-91-8040-594-2 (print)OAI: oai:DiVA.org:kth-327210DiVA, id: diva2:1758348
Public defence
2023-06-12, https://kth-se.zoom.us/j/64592198901, Kollegiesalen, Brinellvägen 8, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20230523

Available from: 2023-05-23 Created: 2023-05-22 Last updated: 2023-06-27Bibliographically approved
List of papers
1. On the programming effort required to generate Behavior Trees and Finite State Machines for robotic applications
Open this publication in new window or tab >>On the programming effort required to generate Behavior Trees and Finite State Machines for robotic applications
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2022 (English)Manuscript (preprint) (Other academic)
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-327208 (URN)
Note

QC 20230523

Available from: 2023-05-22 Created: 2023-05-22 Last updated: 2023-05-23Bibliographically approved
2. Learning Behavior Trees with Genetic Programming in Unpredictable Environments
Open this publication in new window or tab >>Learning Behavior Trees with Genetic Programming in Unpredictable Environments
2021 (English)In: 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2021, p. 459-4597Conference paper, Published paper (Refereed)
Abstract [en]

Modern industrial applications require robots to operate in unpredictable environments, and programs to be created with a minimal effort, to accommodate frequent changes to the task. Here, we show that genetic programming can be effectively used to learn the structure of a behavior tree (BT) to solve a robotic task in an unpredictable environment. We propose to use a simple simulator for learning, and demonstrate that the learned BTs can solve the same task in a realistic simulator, converging without the need for task specific heuristics, making our method appealing for real robotic applications.

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Behavior Trees, Genetic Programming, Mobile Manipulation
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-306057 (URN)10.1109/ICRA48506.2021.9562088 (DOI)000765738803087 ()2-s2.0-85115858496 (Scopus ID)
Conference
2021 IEEE International Conference on Robotics and Automation (ICRA)
Funder
Swedish Foundation for Strategic Research, ID18-0096
Note

Part of proceedings: ISBN 978-1-7281-9077-8, QC 20230118

Available from: 2021-12-13 Created: 2021-12-13 Last updated: 2023-05-22Bibliographically approved
3. Combining Context Awareness and Planning to Learn Behavior Trees from Demonstration
Open this publication in new window or tab >>Combining Context Awareness and Planning to Learn Behavior Trees from Demonstration
2022 (English)In: 2022 31ST IEEE INTERNATIONAL CONFERENCE ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (IEEE RO-MAN 2022), Institute of Electrical and Electronics Engineers Inc. , 2022, p. 1153-1160Conference paper, Published paper (Refereed)
Abstract [en]

Fast changing tasks in unpredictable, collaborative environments are typical for medium-small companies, where robotised applications are increasing. Thus, robot programs should be generated in short time with small effort, and the robot able to react dynamically to the environment. To address this we propose a method that combines context awareness and planning to learn Behavior Trees (BTs), a reactive policy representation that is becoming more popular in robotics and has been used successfully in many collaborative scenarios. Context awareness allows for inferring from the demonstration the frames in which actions are executed and to capture relevant aspects of the task, while a planner is used to automatically generate the BT from the sequence of actions from the demonstration. The learned BT is shown to solve non-trivial manipulation tasks where learning the context is fundamental to achieve the goal. Moreover, we collected non-expert demonstrations to study the performances of the algorithm in industrial scenarios.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2022
Keywords
Behavior Trees, Learning from Demonstration, Manipulation, Collaborative Robotics
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-322437 (URN)10.1109/RO-MAN53752.2022.9900603 (DOI)000885903300165 ()2-s2.0-85138283933 (Scopus ID)
Conference
31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) - Social, Asocial, and Antisocial Robots, AUG 29-SEP 02, 2022, Napoli, ITALY
Note

Part of proceedings: ISBN 978-1-7281-8859-1

QC 20221215

Available from: 2022-12-15 Created: 2022-12-15 Last updated: 2023-05-22Bibliographically approved
4. Interactive Disambiguation for Behavior Tree Execution
Open this publication in new window or tab >>Interactive Disambiguation for Behavior Tree Execution
2022 (English)In: 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids), Institute of Electrical and Electronics Engineers (IEEE) , 2022Conference paper, Published paper (Refereed)
Abstract [en]

Abstract:In recent years, robots are used in an increasing variety of tasks, especially by small- and medium sized enterprises. These tasks are usually fast-changing, they have a collaborative scenario and happen in unpredictable environments with possible ambiguities. It is important to have methods capable of generating robot programs easily, that are made as general as possible by handling uncertainties. We present a system that integrates a method to learn Behavior Trees (BTs) from demonstration for pick and place tasks, with a framework that uses verbal interaction to ask follow-up clarification questions to resolve ambiguities. During the execution of a task, the system asks for user input when there is need to disambiguate an object in the scene, i.e. when the targets of the task are objects of a same type that are present in multiple instances. The integrated system is demonstrated on different scenarios of a pick and place task, with increasing level of ambiguities. The code used for this paper is made publicly available 1 1 https://github.com/matiov/disambiguate-BT-execution.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-323057 (URN)10.1109/Humanoids53995.2022.10000088 (DOI)000925894300011 ()2-s2.0-85146320020 (Scopus ID)
Conference
2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)
Note

QC 20230116

Available from: 2023-01-12 Created: 2023-01-12 Last updated: 2023-05-22Bibliographically approved
5. A Framework for Learning Behavior Trees in Collaborative Robotic Applications
Open this publication in new window or tab >>A Framework for Learning Behavior Trees in Collaborative Robotic Applications
(English)Manuscript (preprint) (Other academic)
Abstract [en]

In modern industrial collaborative robotic applications, it is desirable to create robot programs automatically,intuitively, and time-efficiently. Moreover, robots need to becontrolled by reactive policies to face the unpredictability ofthe environment they operate in. In this paper we proposea framework that combines a method that learns BehaviorTrees (BTs) from demonstration with a method that evolvesthem with Genetic Programming (GP) for collaborative roboticapplications. The main contribution of this paper is to show thatby combining the two learning methods we obtain a method thatallows non-expert users to semi-automatically, time-efficiently,and interactively generate BTs. We validate the framework witha series of manipulation experiments. The BT is fully learnt insimulation and then transferred to a real collaborative robot.

Keywords
Behavior Trees, Genetic Programming, Learning from Demonstration, Collaborative Robotics
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-327202 (URN)
Funder
Swedish Foundation for Strategic ResearchKnut and Alice Wallenberg Foundation
Note

Submitted to the IEEE for possible publication

QC 20230525

Available from: 2023-05-22 Created: 2023-05-22 Last updated: 2023-05-25Bibliographically approved

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Iovino, Matteo

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