{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T04:09:51Z","timestamp":1743134991244,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819603503"},{"type":"electronic","value":"9789819603510"}],"license":[{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-0351-0_1","type":"book-chapter","created":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T18:47:24Z","timestamp":1732387644000},"page":"3-15","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ECoDe: A Sample-Efficient Method for\u00a0Co-design of\u00a0Robotic Agents"],"prefix":"10.1007","author":[{"given":"Kishan Reddy","family":"Nagiredla","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Buddhika Laknath","family":"Semage","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arun Kumar","family":"Anjanapura Venkatesh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thommen George","family":"Karimpanal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Santu","family":"Rana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,20]]},"reference":[{"unstructured":"Andrychowicz, O.M., et\u00a0al.: Learning dexterous in-hand manipulation. Int. J. Robot. Res. 39 (2020)","key":"1_CR1"},{"key":"1_CR2","first-page":"2201","volume":"34","author":"J Bhatia","year":"2021","unstructured":"Bhatia, J., Jackson, H., Tian, Y., Xu, J., Matusik, W.: Evolution gym: a large-scale benchmark for evolving soft robots. Neural Inf. Process. Syst. 34, 2201\u20132214 (2021)","journal-title":"Neural Inf. Process. Syst."},{"unstructured":"Brockman, G., et al.: OpenAI gym. arXiv e-prints (2016)","key":"1_CR3"},{"issue":"4","key":"1_CR4","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1162\/artl_a_00301","volume":"25","author":"D Ha","year":"2019","unstructured":"Ha, D.: Reinforcement learning for improving agent design. Artif. Life 25(4), 352\u2013365 (2019)","journal-title":"Artif. Life"},{"unstructured":"Jamieson, K., Talwalkar, A.: Non-stochastic best arm identification and hyperparameter optimization. In: Artificial Intelligence and Statistics (2016)","key":"1_CR5"},{"doi-asserted-by":"crossref","unstructured":"Jittorntrum, K.: An implicit function theorem. J. Optim. Theory Appli. 25(4) (1978)","key":"1_CR6","DOI":"10.1007\/BF00933522"},{"issue":"1","key":"1_CR7","first-page":"1","volume":"18","author":"L Li","year":"2017","unstructured":"Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., Talwalkar, A.: Hyperband: a novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res. 18(1), 1\u201352 (2017)","journal-title":"J. Mach. Learn. Res."},{"unstructured":"Luck, K.S., Amor, H.B., Calandra, R.: Data-efficient co-adaptation of morphology and behaviour with deep reinforcement learning. In: Conference on Robot Learning. PMLR (2020)","key":"1_CR8"},{"unstructured":"Pelikan, M., Goldberg, D.E., Cant\u00fa-Paz, E., et\u00a0al.: BOA: the bayesian optimization algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference GECCO-99, vol.\u00a01 (1999)","key":"1_CR9"},{"doi-asserted-by":"crossref","unstructured":"Schaff, C., Yunis, D., Chakrabarti, A., Walter, M.R.: Jointly learning to construct and control agents using deep reinforcement learning. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 9798\u20139805. IEEE (2019)","key":"1_CR10","DOI":"10.1109\/ICRA.2019.8793537"},{"unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)","key":"1_CR11"},{"doi-asserted-by":"crossref","unstructured":"Semage, B.L., Karimpanal, T.G., Rana, S., Venkatesh, S.: Fast model-based policy search for universal policy networks. In: 2022 26th International Conference on Pattern Recognition (ICPR). IEEE (2022)","key":"1_CR12","DOI":"10.1109\/ICPR56361.2022.9956692"},{"doi-asserted-by":"crossref","unstructured":"Sims, K.: Evolving virtual creatures. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques (1994)","key":"1_CR13","DOI":"10.1145\/192161.192167"},{"doi-asserted-by":"crossref","unstructured":"Todorov, E., Erez, T., Tassa, Y.: MuJoCo: a physics engine for model-based control. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems (2012)","key":"1_CR14","DOI":"10.1109\/IROS.2012.6386109"},{"issue":"5","key":"1_CR15","doi-asserted-by":"publisher","first-page":"1592","DOI":"10.1109\/TMECH.2012.2208196","volume":"18","author":"MG Villarreal-Cervantes","year":"2012","unstructured":"Villarreal-Cervantes, M.G., Cruz-Villar, C.A., Alvarez-Gallegos, J., Portilla-Flores, E.A.: Robust structure-control design approach for mechatronic systems. IEEE\/ASME Trans. Mechatron. 18(5), 1592\u20131601 (2012)","journal-title":"IEEE\/ASME Trans. Mechatron."},{"unstructured":"Von\u00a0Neumann, J., Burks, A.W.: Theory of self-reproducing automata. IEEE Trans. Neural Netw. 5(1) (1996)","key":"1_CR16"},{"unstructured":"Yu, T., Zhu, H.: Hyper-parameter optimization: a review of algorithms and applications. arXiv preprint arXiv:2003.05689 (2020)","key":"1_CR17"},{"issue":"2","key":"1_CR18","doi-asserted-by":"publisher","first-page":"2950","DOI":"10.1109\/LRA.2020.2974685","volume":"5","author":"W Yu","year":"2020","unstructured":"Yu, W., Tan, J., Bai, Y., Coumans, E., Ha, S.: Learning fast adaptation with meta strategy optimization. IEEE Robot. Autom. Lett. 5(2), 2950\u20132957 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"unstructured":"Yuan, Y., Song, Y., Luo, Z., Sun, W., Kitani, K.M.: Transform2Act: learning a transform-and-control policy for efficient agent design. In: International Conference on Learning Representations (2021)","key":"1_CR19"},{"doi-asserted-by":"crossref","unstructured":"Zhao, W., Queralta, J.P., Westerlund, T.: Sim-to-real transfer in deep reinforcement learning for robotics: a survey. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 737\u2013744 (2020)","key":"1_CR20","DOI":"10.1109\/SSCI47803.2020.9308468"}],"container-title":["Lecture Notes in Computer Science","AI 2024: Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0351-0_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T19:04:54Z","timestamp":1732388694000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0351-0_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,20]]},"ISBN":["9789819603503","9789819603510"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0351-0_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,20]]},"assertion":[{"value":"20 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Joint Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Melbourne, VIC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"37","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ausai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ajcai2024.org\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}