{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:22:57Z","timestamp":1780053777575,"version":"3.54.0"},"reference-count":66,"publisher":"Annual Reviews","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Annu. Rev. Control Robot. Auton. Syst."],"published-print":{"date-parts":[[2022,5,3]]},"abstract":"<jats:p>In this article, we provide a control-theoretic perspective on the research area of behavior trees in robotics. The key idea underlying behavior trees is to make use of modularity, hierarchies, and feedback in order to handle the complexity of a versatile robot control system. Modularity is a well-known tool to handle software complexity by enabling the development, debugging, and extension of separate modules without detailed knowledge of the entire system. A hierarchy of such modules is natural, since robot tasks can often be decomposed into a hierarchy of subtasks. Finally, feedback control is a fundamental tool for handling uncertainties and disturbances in any low-level control system, but in order to enable feedback control on the higher level, where one module decides what submodule to execute, information regarding the progress and applicability of each submodule needs to be shared in the module interfaces. We describe how these three concepts can be used in theoretical analysis, practical design, and extensions and combinations with other ideas from control theory and robotics.<\/jats:p>","DOI":"10.1146\/annurev-control-042920-095314","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T18:35:50Z","timestamp":1638902150000},"page":"81-107","source":"Crossref","is-referenced-by-count":50,"title":["Behavior Trees in Robot Control Systems"],"prefix":"10.1146","volume":"5","author":[{"given":"Petter","family":"\u00d6gren","sequence":"first","affiliation":[{"name":"Division of Robotics, Perception, and Learning, KTH Royal Institute of Technology, Stockholm, Sweden;,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher I.","family":"Sprague","sequence":"additional","affiliation":[{"name":"Division of Robotics, Perception, and Learning, KTH Royal Institute of Technology, Stockholm, Sweden;,"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"22","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1145\/325478.325518"},{"key":"B2","volume-title":"A structure for plans and behavior","author":"Sacerdoti ED.","year":"1975"},{"key":"B3","unstructured":"Erol K, Hendler J, Nau DS. 1994. UMCP: a sound and complete procedure for hierarchical task-network planning. InProceedings of the Second International Conference on Artificial Intelligence Planning Systems, pp. 249\u201354. Palo Alto, CA: AAAI Press"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2002.1024751"},{"key":"B5","unstructured":"Isla D. 2005.Handling complexity in the Halo 2 AI. Paper presented at the Game Developers Conference, San Francisco, Mar. 7\u201311.https:\/\/www.gdcvault.com\/play\/1020270\/Managing-Complexity-in-the-Halo"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1109\/TCIAIG.2009.2036369"},{"key":"B7","doi-asserted-by":"crossref","unstructured":"\u00d6gren P. 2012. Increasing modularity of UAV control systems using computer game behavior trees. InAIAA Guidance, Navigation, and Control Conference, pap. 2012-4458. Reston, VA: Am. Inst. Aeronaut. Astronaut.","DOI":"10.2514\/6.2012-4458"},{"key":"B8","doi-asserted-by":"crossref","unstructured":"Bagnell JA, Cavalcanti F, Cui L, Galluzzo T, Hebert M, et al. 2012. An integrated system for autonomous robotics manipulation. In2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 2955\u201362. Piscataway, NJ: IEEE","DOI":"10.1109\/IROS.2012.6385888"},{"key":"B9","author":"Wikipedia","year":"2020","journal-title":"Wikipedia"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/0167-6423(87)90035-9"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3074337"},{"key":"B12","unstructured":"Iovino M, Scukins E, Styrud J, \u00d6gren P, Smith C. 2020. A survey of behavior trees in robotics and AI. arXiv:2005.05842 [cs.RO]"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1201\/9780429489105"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2016.2633567"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3010747"},{"key":"B16","doi-asserted-by":"crossref","unstructured":"Sprague CI, \u00d6gren P. 2021. Continuous-time behavior trees as discontinuous dynamical systems. arXiv:2109.01575 [eess.SY]","DOI":"10.1109\/LCSYS.2021.3134453"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3087442"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1109\/MCS.2008.919306"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-015-7793-9"},{"key":"B20","unstructured":"Biggar O, Zamani M, Shames I. 2020. On modularity in reactive control architectures, with an application to formal verification. arXiv:2008.12515 [cs.AI]"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1007\/BF02020961"},{"key":"B22","volume-title":"Structured testing: a testing methodology using the cyclomatic complexity metric","author":"Watson AH","year":"1996"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1177\/02783649922066385"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2006.II.008"},{"key":"B25","doi-asserted-by":"crossref","unstructured":"Reist P, Tedrake R. 2010. Simulation-based LQR-trees with input and state constraints. In2010 IEEE International Conference on Robotics and Automation, pp. 5504\u201310. Piscataway, NJ: IEEE","DOI":"10.1109\/ROBOT.2010.5509893"},{"key":"B26","doi-asserted-by":"crossref","unstructured":"Paxton C, Ratliff N, Eppner C, Fox D. 2019. Representing robot task plans as robust logical-dynamical systems. In2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5588\u201395. Piscataway, NJ: IEEE","DOI":"10.1109\/IROS40897.2019.8967861"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.23919\/ECC.2019.8796030"},{"key":"B28","doi-asserted-by":"crossref","unstructured":"\u00d6gren P. 2006. Autonomous UCAV strike missions using behavior control Lyapunov functions. InAIAA Guidance, Navigation, and Control Conference, pap. 2006-6197. Reston, VA: Am. Inst. Aeronaut. Astronaut.","DOI":"10.2514\/6.2006-6197"},{"key":"B29","doi-asserted-by":"crossref","unstructured":"\u00d6zkahraman, \u00d6gren P. 2020. Combining control barrier functions and behavior trees for multi-agent underwater coverage missions. In2020 59th IEEE Conference on Decision and Control (CDC), pp. 5275\u201382. Piscataway, NJ: IEEE","DOI":"10.1109\/CDC42340.2020.9304151"},{"key":"B30","volume-title":"Autonomous horizons: system autonomy in the Air Force \u2013 a path to the future. Volume I: human-autonomy teaming","author":"Endsley MR.","year":"2015"},{"key":"B31","unstructured":"Paxton C, Jonathan F, Hundt A, Mutlu B, Hager GD. 2017. User experience of the CoSTAR system for instruction of collaborative robots. arXiv:1703.07890 [cs.RO]"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1724-z"},{"key":"B33","unstructured":"Pereira RDP, Engel PM. 2015. A framework for constrained and adaptive behavior-based agents. arXiv:1506.02312 [cs.AI]"},{"key":"B34","volume-title":"Integrating reinforcement learning into behavior trees by hierarchical composition","author":"Kartasev M.","year":"2019"},{"key":"B35","unstructured":"Sprague CI, \u00d6gren P. 2018. Adding neural network controllers to behavior trees without destroying performance guarantees. arXiv:1809.10283 [cs.RO]"},{"key":"B36","doi-asserted-by":"crossref","unstructured":"Dey R, Child C. 2013. QL-BT: enhancing behaviour tree design and implementation with Q-learning. In2013 IEEE Conference on Computational Intelligence in Games (CIG). Piscataway, NJ: IEEE.https:\/\/doi.org\/10.1109\/CIG.2013.6633623","DOI":"10.1109\/CIG.2013.6633623"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.2991\/essaeme-16.2016.120"},{"key":"B38","doi-asserted-by":"crossref","unstructured":"Zhang Q, Sun L, Jiao P, Yin Q. 2017. Combining behavior trees with MAXQ learning to facilitate CGFs behavior modeling. In2017 4th International Conference on Systems and Informatics (ICSAI), pp. 525\u201331. Piscataway, NJ: IEEE","DOI":"10.1109\/ICSAI.2017.8248348"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-179190"},{"key":"B40","unstructured":"Hannaford B, Hu D, Zhang D, Li Y. 2016. Simulation results on selector adaptation in behavior trees. arXiv:1606.09219 [cs.RO]"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1007\/BF00175354"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1007\/11729976_29"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.1162\/evco.2006.14.3.309"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1109\/TG.2018.2816806"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-43722-0_24"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-12239-2_11"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.1109\/TCIAIG.2016.2543661"},{"key":"B48","doi-asserted-by":"crossref","unstructured":"Iovino M, Styrud J, Falco P, Smith C. 2020. Learning behavior trees with genetic programming in unpredictable environments. arXiv:2011.03252 [cs.RO]","DOI":"10.1109\/ICRA48506.2021.9562088"},{"key":"B49","doi-asserted-by":"crossref","unstructured":"Paduraru C, Paduraru M. 2019. Automatic difficulty management and testing in games using a framework based on behavior trees and genetic algorithms. arXiv:1909.04368 [cs.AI]","DOI":"10.1109\/ICECCS.2019.00026"},{"key":"B50","doi-asserted-by":"crossref","unstructured":"Styrud J, Iovino M, Norrl\u00f6f M, Bj\u00f6rkman M, Smith C. 2021. Combining planning and learning of behavior trees for robotic assembly. arXiv:2103.09036 [cs.LG]","DOI":"10.1109\/ICRA46639.2022.9812086"},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/73"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-73008-0_34"},{"key":"B53","unstructured":"Mart\u00edn F, Morelli M, Espinoza H, Lera FJR, Matell\u00e1n V. 2021. Optimized execution of PDDL plans using behavior trees. arXiv:2101.01964 [cs.RO]"},{"key":"B54","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794128"},{"key":"B55","doi-asserted-by":"crossref","unstructured":"Tadewos TG, Shamgah L, Karimoddini A. 2019. Automatic safe behaviour tree synthesis for autonomous agents. In2019 IEEE 58th Conference on Decision and Control (CDC), pp. 2776\u201381. Piscataway, NJ: IEEE","DOI":"10.1109\/CDC40024.2019.9030183"},{"key":"B56","doi-asserted-by":"crossref","unstructured":"Tadewos TG, Shamgah L, Karimoddini A. 2019. On-the-fly decentralized tasking of autonomous vehicles. In2019 IEEE 58th Conference on Decision and Control (CDC), pp. 2770\u201375. Piscataway, NJ: IEEE","DOI":"10.1109\/CDC40024.2019.9029554"},{"key":"B57","doi-asserted-by":"crossref","unstructured":"Zhou H, Min H, Lin Y. 2019. An autonomous task algorithm based on behavior trees for robot. In2019 2nd China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI), pp. 64\u201370. Piscataway, NJ: IEEE","DOI":"10.1109\/CCHI.2019.8901959"},{"key":"B58","doi-asserted-by":"crossref","unstructured":"Schwab P, Hlavacs H. 2015. Capturing the essence: towards the automated generation of transparent behavior models. InProceedings of the Eleventh Artificial Intelligence and Interactive Digital Entertainment Conference, pp. 184\u201390. Reston, VA: Am. Inst. Aeronaut. Astronaut.","DOI":"10.1609\/aiide.v11i1.12795"},{"key":"B59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00533-7_3"},{"key":"B60","doi-asserted-by":"crossref","unstructured":"Neufeld X, Mostaghim S, Brand S. 2018. A hybrid approach to planning and execution in dynamic environments through hierarchical task networks and behavior trees. InProceedings of the Fourteenth Artificial Intelligence and Interactive Digital Entertainment Conference, pp. 201\u20137. Reston, VA: Am. Inst. Aeronaut. Astronaut.","DOI":"10.1609\/aiide.v14i1.13044"},{"key":"B61","doi-asserted-by":"crossref","unstructured":"Rovida F, Grossmann B, Kr\u00fcger V. 2017. Extended behavior trees for quick definition of flexible robotic tasks. In2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6793\u2013800. Piscataway, NJ: IEEE","DOI":"10.1109\/IROS.2017.8206598"},{"key":"B62","doi-asserted-by":"crossref","unstructured":"Segura-Muros J\u00c1, Fern\u00e1ndez-Olivares J. 2017. Integration of an automated hierarchical task planner in ROS using behaviour trees. In2017 6th International Conference on Space Mission Challenges for Information Technology (SMC-IT), pp. 20\u201325. Piscataway, NJ: IEEE","DOI":"10.1109\/SMC-IT.2017.11"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-16310-9_7"},{"key":"B64","doi-asserted-by":"crossref","unstructured":"Colledanchise M, Murray RM, \u00d6gren P. 2017. Synthesis of correct-by-construction behavior trees. In2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6039\u201346. Piscataway, NJ: IEEE","DOI":"10.1109\/IROS.2017.8206502"},{"key":"B65","doi-asserted-by":"crossref","unstructured":"Lan M, Lai S, Lee TH, Chen BM. 2019. Autonomous task planning and acting for micro aerial vehicles. In2019 IEEE 15th International Conference on Control and Automation (ICCA), pp. 738\u201345. Piscataway, NJ: IEEE","DOI":"10.1109\/ICCA.2019.8899502"},{"key":"B66","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2970634"}],"container-title":["Annual Review of Control, Robotics, and Autonomous Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.annualreviews.org\/doi\/pdf\/10.1146\/annurev-control-042920-095314","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T15:28:39Z","timestamp":1673969319000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.annualreviews.org\/doi\/10.1146\/annurev-control-042920-095314"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,3]]},"references-count":66,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,5,3]]}},"alternative-id":["10.1146\/annurev-control-042920-095314"],"URL":"https:\/\/doi.org\/10.1146\/annurev-control-042920-095314","relation":{},"ISSN":["2573-5144","2573-5144"],"issn-type":[{"value":"2573-5144","type":"print"},{"value":"2573-5144","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,3]]}}}