{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T13:23:36Z","timestamp":1780406616860,"version":"3.54.1"},"reference-count":68,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T00:00:00Z","timestamp":1606435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100011039","name":"Intelligence Advanced Research Projects Activity","doi-asserted-by":"publisher","award":["2019-19020100001"],"award-info":[{"award-number":["2019-19020100001"]}],"id":[{"id":"10.13039\/100011039","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science Foundation","award":["1644558"],"award-info":[{"award-number":["1644558"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>\n            We present\n            <jats:italic>RoboGrammar<\/jats:italic>\n            , a fully automated approach for generating optimized robot structures to traverse given terrains. In this framework, we represent each robot design as a graph, and use a graph grammar to express possible arrangements of physical robot assemblies. Each robot design can then be expressed as a sequence of grammar rules. Using only a small set of rules our grammar can describe hundreds of thousands of possible robot designs. The construction of the grammar limits the design space to designs that can be fabricated. For a given input terrain, the design space is searched to find the top performing robots and their corresponding controllers. We introduce Graph Heuristic Search - a novel method for efficient search of combinatorial design spaces. In Graph Heuristic Search, we explore the design space while simultaneously learning a function that maps incomplete designs (e.g., nodes in the combinatorial search tree) to the best performance values that can be achieved by expanding these incomplete designs. Graph Heuristic Search prioritizes exploration of the most promising branches of the design space. To test our method we optimize robots for a number of challenging and varied terrains. We demonstrate that\n            <jats:italic>RoboGrammar<\/jats:italic>\n            can successfully generate nontrivial robots that are optimized for a single terrain or a combination of terrains.\n          <\/jats:p>","DOI":"10.1145\/3414685.3417831","type":"journal-article","created":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T21:51:05Z","timestamp":1606513865000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":138,"title":["RoboGrammar"],"prefix":"10.1145","volume":"39","author":[{"given":"Allan","family":"Zhao","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Xu","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mina","family":"Konakovi\u0107-Lukovi\u0107","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josephine","family":"Hughes","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Spielberg","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniela","family":"Rus","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wojciech","family":"Matusik","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1002\/1097-4563(200102)18:2<77::AID-ROB1007>3.0.CO;2-A"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1778765.1778841"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2493883"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0128444"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCIAIG.2012.2186810"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.1995.525275"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CIG.2008.5035667"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1956.1056813"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2776880.2792704"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818069"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1068\/b080269"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1068\/b31124"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1177\/027836498300200102"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/0005-1098(89)90002-2"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2093548.2093574"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2011.03.007"},{"key":"e_1_2_2_17_1","volume-title":"Reinforcement learning for improving agent design. arXiv preprint arXiv:1810.03779","author":"David Ha.","year":"2018","unstructured":"David Ha. 2018. Reinforcement learning for improving agent design. arXiv preprint arXiv:1810.03779 ( 2018 ). David Ha. 2018. Reinforcement learning for improving agent design. arXiv preprint arXiv:1810.03779 (2018)."},{"key":"e_1_2_2_18_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in neural information processing systems. 1024--1034.  Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in neural information processing systems. 1024--1034."},{"key":"e_1_2_2_19_1","unstructured":"David P Helmbold and Aleatha Parker-Wood. 2009. All-Moves-As-First Heuristics in Monte-Carlo Go.. In IC-AI. 605--610.  David P Helmbold and Aleatha Parker-Wood. 2009. All-Moves-As-First Heuristics in Monte-Carlo Go.. In IC-AI. 605--610."},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2011.2172702"},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2001.934446"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-016-1254-8"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-018-9738-1"},{"key":"e_1_2_2_24_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba . 2015 . Adam : A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds .). http:\/\/arxiv.org\/abs\/1412.6980 Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"e_1_2_2_25_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 ( 2016 ). Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_2_2_26_1","volume-title":"Tracking-error model-based predictive control for mobile robots in real time. Robotics and autonomous systems 55, 6","author":"Klan\u010dar Gregor","year":"2007","unstructured":"Gregor Klan\u010dar and Igor \u0160krjanc . 2007. Tracking-error model-based predictive control for mobile robots in real time. Robotics and autonomous systems 55, 6 ( 2007 ), 460--469. Gregor Klan\u010dar and Igor \u0160krjanc. 2007. Tracking-error model-based predictive control for mobile robots in real time. Robotics and autonomous systems 55, 6 (2007), 460--469."},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2004.1302558"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/11871842_29"},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/WESCON.1995.485447"},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2010.01714.x"},{"key":"e_1_2_2_31_1","volume-title":"Proceedings of mechatronics and robotics. Citeseer, 525--530","author":"Kuhne Felipe","year":"2004","unstructured":"Felipe Kuhne , Walter Fetter Lages , and J Gomes da Silva Jr . 2004 . Model predictive control of a mobile robot using linearization . In Proceedings of mechatronics and robotics. Citeseer, 525--530 . Felipe Kuhne, Walter Fetter Lages, and J Gomes da Silva Jr. 2004. Model predictive control of a mobile robot using linearization. In Proceedings of mechatronics and robotics. Citeseer, 525--530."},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964921.1964980"},{"key":"e_1_2_2_33_1","volume-title":"Learning heuristics for automated reasoning through deep reinforcement learning. arXiv preprint arXiv:1807.08058","author":"Lederman Gil","year":"2018","unstructured":"Gil Lederman , Markus N Rabe , Edward A Lee , and Sanjit A Seshia . 2018. Learning heuristics for automated reasoning through deep reinforcement learning. arXiv preprint arXiv:1807.08058 ( 2018 ). Gil Lederman, Markus N Rabe, Edward A Lee, and Sanjit A Seshia. 2018. Learning heuristics for automated reasoning through deep reinforcement learning. arXiv preprint arXiv:1807.08058 (2018)."},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/1399504.1360701"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2661229.2661243"},{"key":"e_1_2_2_36_1","volume-title":"Plan online, learn offline: Efficient learning and exploration via modelbased control. arXiv preprint arXiv:1811.01848","author":"Lowrey Kendall","year":"2018","unstructured":"Kendall Lowrey , Aravind Rajeswaran , Sham Kakade , Emanuel Todorov , and Igor Mordatch . 2018. Plan online, learn offline: Efficient learning and exploration via modelbased control. arXiv preprint arXiv:1811.01848 ( 2018 ). Kendall Lowrey, Aravind Rajeswaran, Sham Kakade, Emanuel Todorov, and Igor Mordatch. 2018. Plan online, learn offline: Efficient learning and exploration via modelbased control. arXiv preprint arXiv:1811.01848 (2018)."},{"key":"e_1_2_2_37_1","volume-title":"Proceedings of Futureground, Design Research Society","author":"McCormack Jon","year":"2004","unstructured":"Jon McCormack , Alan Dorin , Troy Innocent , 2004 . Generative design: a paradigm for design research . Proceedings of Futureground, Design Research Society , Melbourne (2004). Jon McCormack, Alan Dorin, Troy Innocent, et al. 2004. Generative design: a paradigm for design research. Proceedings of Futureground, Design Research Society, Melbourne (2004)."},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/1141911.1141931"},{"key":"e_1_2_2_39_1","doi-asserted-by":"crossref","unstructured":"R\u00e9mi Munos et al. 2014. From bandits to Monte-Carlo Tree Search: The optimistic principle applied to optimization and planning. Foundations and Trends\u00ae in Machine Learning 7 1 (2014) 1--129.  R\u00e9mi Munos et al. 2014. From bandits to Monte-Carlo Tree Search: The optimistic principle applied to optimization and planning. Foundations and Trends\u00ae in Machine Learning 7 1 (2014) 1--129.","DOI":"10.1561\/2200000038"},{"key":"e_1_2_2_40_1","volume-title":"Long-term robot motion planning for active sound source localization with Monte Carlo tree search. In 2017 Hands-free Speech Communications and Microphone Arrays (HSCMA)","author":"Nguyen Quan V","unstructured":"Quan V Nguyen , Francis Colas , Emmanuel Vincent , and Fran\u00e7ois Charpillet . 2017. Long-term robot motion planning for active sound source localization with Monte Carlo tree search. In 2017 Hands-free Speech Communications and Microphone Arrays (HSCMA) . IEEE , 61--65. Quan V Nguyen, Francis Colas, Emmanuel Vincent, and Fran\u00e7ois Charpillet. 2017. Long-term robot motion planning for active sound source localization with Monte Carlo tree search. In 2017 Hands-free Speech Communications and Microphone Arrays (HSCMA). IEEE, 61--65."},{"key":"e_1_2_2_41_1","volume-title":"Artificial Intelligence: A modern approach","author":"Norvig Peter","year":"2002","unstructured":"Peter Norvig and Stuart Russell . 2002 . Artificial Intelligence: A modern approach ( 3 rd ed.). Prentice Hall . Peter Norvig and Stuart Russell. 2002. Artificial Intelligence: A modern approach (3rd ed.). Prentice Hall.","edition":"3"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/383259.383292"},{"key":"e_1_2_2_43_1","volume-title":"Learning to control self-assembling morphologies: a study of generalization via modularity. arXiv preprint arXiv:1902.05546","author":"Pathak Deepak","year":"2019","unstructured":"Deepak Pathak , Chris Lu , Trevor Darrell , Phillip Isola , and Alexei A Efros . 2019. Learning to control self-assembling morphologies: a study of generalization via modularity. arXiv preprint arXiv:1902.05546 ( 2019 ). Deepak Pathak, Chris Lu, Trevor Darrell, Phillip Isola, and Alexei A Efros. 2019. Learning to control self-assembling morphologies: a study of generalization via modularity. arXiv preprint arXiv:1902.05546 (2019)."},{"key":"e_1_2_2_44_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3073602","article-title":"Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning","volume":"36","author":"Peng Xue Bin","year":"2017","unstructured":"Xue Bin Peng , Glen Berseth , KangKang Yin , and Michiel Van De Panne . 2017 . Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning . ACM Transactions on Graphics (TOG) 36 , 4 (2017), 1 -- 13 . Xue Bin Peng, Glen Berseth, KangKang Yin, and Michiel Van De Panne. 2017. Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning. ACM Transactions on Graphics (TOG) 36, 4 (2017), 1--13.","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1162\/002409403321554170"},{"key":"e_1_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/SIS.2007.367956"},{"key":"e_1_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-87608-3_1"},{"key":"e_1_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793537"},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01589682"},{"key":"e_1_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.1315299"},{"key":"e_1_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/192161.192167"},{"key":"e_1_2_2_52_1","volume-title":"Introduction to genetic algorithms","author":"Sivanandam SN","unstructured":"SN Sivanandam and SN Deepa . 2008. Genetic algorithms . In Introduction to genetic algorithms . Springer , 15--37. SN Sivanandam and SN Deepa. 2008. Genetic algorithms. In Introduction to genetic algorithms. Springer, 15--37."},{"key":"e_1_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-008-9107-6"},{"key":"e_1_2_2_54_1","unstructured":"Andrew Spielberg Allan Zhao Yuanming Hu Tao Du Wojciech Matusik and Daniela Rus. 2019. Learning-In-The-Loop Optimization: End-To-End Control And Co-Design of Soft Robots Through Learned Deep Latent Representations. In Advances in Neural Information Processing Systems. 8282--8292.  Andrew Spielberg Allan Zhao Yuanming Hu Tao Du Wojciech Matusik and Daniela Rus. 2019. Learning-In-The-Loop Optimization: End-To-End Control And Co-Design of Soft Robots Through Learned Deep Latent Representations. In Advances in Neural Information Processing Systems. 8282--8292."},{"key":"e_1_2_2_55_1","volume-title":"Computer Graphics Forum","author":"\u0160t'ava Ondrej","unstructured":"Ondrej \u0160t'ava , Bedrich Bene\u0161 , Radomir M\u011bch , Daniel G Aliaga , and Peter Kri\u0161tof . 2010. Inverse procedural modeling by automatic generation of L-systems . In Computer Graphics Forum , Vol. 29 . Wiley Online Library , 665--674. Ondrej \u0160t'ava, Bedrich Bene\u0161, Radomir M\u011bch, Daniel G Aliaga, and Peter Kri\u0161tof. 2010. Inverse procedural modeling by automatic generation of L-systems. In Computer Graphics Forum, Vol. 29. Wiley Online Library, 665--674."},{"key":"e_1_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1068\/b090113"},{"key":"e_1_2_2_57_1","volume-title":"IFIP Congress 71","author":"Stiny George","year":"1971","unstructured":"George Stiny and James Gips . 1971 . ' Shape Grammars and the Generative Specification of Painting and Sculpture '. IFIP Congress 71 , 1460--1465. George Stiny and James Gips. 1971. 'Shape Grammars and the Generative Specification of Painting and Sculpture'. IFIP Congress 71, 1460--1465."},{"key":"e_1_2_2_58_1","volume-title":"Mitchell","author":"Stiny George","year":"1978","unstructured":"George Stiny and William J . Mitchell . 1978 . The Palladian Grammar . George Stiny and William J. Mitchell. 1978. The Palladian Grammar."},{"key":"e_1_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1115\/DETC2015-47641"},{"key":"e_1_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2004.1389841"},{"key":"e_1_2_2_61_1","article-title":"A spatial grammar method for the computational design synthesis of virtual soft locomotion robots","volume":"141","author":"Diepen Merel Van","year":"2019","unstructured":"Merel Van Diepen and Kristina Shea . 2019 . A spatial grammar method for the computational design synthesis of virtual soft locomotion robots . Journal of Mechanical Design 141 , 10 (2019). Merel Van Diepen and Kristina Shea. 2019. A spatial grammar method for the computational design synthesis of virtual soft locomotion robots. Journal of Mechanical Design 141, 10 (2019).","journal-title":"Journal of Mechanical Design"},{"key":"e_1_2_2_62_1","volume-title":"Paired open-ended trailblazer (poet): Endlessly generating increasingly complex and diverse learning environments and their solutions. arXiv preprint arXiv:1901.01753","author":"Wang Rui","year":"2019","unstructured":"Rui Wang , Joel Lehman , Jeff Clune , and Kenneth O Stanley . 2019a. Paired open-ended trailblazer (poet): Endlessly generating increasingly complex and diverse learning environments and their solutions. arXiv preprint arXiv:1901.01753 ( 2019 ). Rui Wang, Joel Lehman, Jeff Clune, and Kenneth O Stanley. 2019a. Paired open-ended trailblazer (poet): Endlessly generating increasingly complex and diverse learning environments and their solutions. arXiv preprint arXiv:1901.01753 (2019)."},{"key":"e_1_2_2_63_1","volume-title":"Neural Graph Evolution: Towards Efficient Automatic Robot Design. arXiv preprint arXiv:1906.05370","author":"Wang Tingwu","year":"2019","unstructured":"Tingwu Wang , Yuhao Zhou , Sanja Fidler , and Jimmy Ba. 2019b. Neural Graph Evolution: Towards Efficient Automatic Robot Design. arXiv preprint arXiv:1906.05370 ( 2019 ). Tingwu Wang, Yuhao Zhou, Sanja Fidler, and Jimmy Ba. 2019b. Neural Graph Evolution: Towards Efficient Automatic Robot Design. arXiv preprint arXiv:1906.05370 (2019)."},{"key":"e_1_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0921-8890(02)00170-7"},{"key":"e_1_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2016.7487277"},{"key":"e_1_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/882262.882324"},{"key":"e_1_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601162"},{"key":"e_1_2_2_68_1","unstructured":"Zhitao Ying Jiaxuan You Christopher Morris Xiang Ren Will Hamilton and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In Advances in neural information processing systems. 4800--4810.  Zhitao Ying Jiaxuan You Christopher Morris Xiang Ren Will Hamilton and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In Advances in neural information processing systems. 4800--4810."}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3414685.3417831","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3414685.3417831","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3414685.3417831","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:03:15Z","timestamp":1750197795000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3414685.3417831"}},"subtitle":["graph grammar for terrain-optimized robot design"],"short-title":[],"issued":{"date-parts":[[2020,11,27]]},"references-count":68,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2020,12,31]]}},"alternative-id":["10.1145\/3414685.3417831"],"URL":"https:\/\/doi.org\/10.1145\/3414685.3417831","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,27]]},"assertion":[{"value":"2020-11-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}