{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T23:47:01Z","timestamp":1767138421185,"version":"build-2238731810"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031159183","type":"print"},{"value":"9783031159190","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Collecting large amounts of training data with a real robot to learn visuomotor abilities is time-consuming and limited by expensive robotic hardware. Simulators provide a safe, distributable way to collect data, but due to discrepancies between simulation and reality, learned strategies often do not transfer to the real world. This paper examines whether domain randomisation can increase the real-world performance of a model trained entirely in simulation without additional fine-tuning. We replicate a reach-to-grasp experiment with the NICO humanoid robot in simulation and develop a method to autonomously create training data for a supervised learning approach with an end-to-end convolutional neural architecture. We compare model performance and real-world transferability for different amounts of data and randomisation conditions. Our results show that domain randomisation improves the transferability of a model and can mitigate negative effects of overfitting.<\/jats:p>","DOI":"10.1007\/978-3-031-15919-0_29","type":"book-chapter","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:04:59Z","timestamp":1662422699000},"page":"342-354","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Sim-to-Real Neural Learning with\u00a0Domain Randomisation for\u00a0Humanoid Robot Grasping"],"prefix":"10.1007","author":[{"given":"Connor","family":"G\u00e4de","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthias","family":"Kerzel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erik","family":"Strahl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"Wermter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"29_CR1","doi-asserted-by":"crossref","unstructured":"van Baar, J., Sullivan, A., Cordorel, R., Jha, D., Romeres, D., Nikovski, D.: Sim-to-real transfer learning using robustified controllers in robotic tasks involving complex dynamics. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 6001\u20136007. IEEE (2019)","DOI":"10.1109\/ICRA.2019.8793561"},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Bergstra, J., Yamins, D., Cox, D.D., et al.: Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms. In: Proceedings of the 12th Python in Science Conference, vol. 13, p. 20. Citeseer (2013)","DOI":"10.25080\/Majora-8b375195-003"},{"key":"29_CR3","unstructured":"James, S., Davison, A.J., Johns, E.: Transferring end-to-end visuomotor control from simulation to real world for a multi-stage task. In: Conference on Robot Learning, pp. 334\u2013343. PMLR (2017)"},{"key":"29_CR4","unstructured":"James, S., Freese, M., Davison, A.J.: Pyrep: bringing v-rep to deep robot learning. arXiv preprint arXiv:1906.11176 (2019)"},{"key":"29_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/978-3-030-61616-8_43","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2020","author":"M Kerzel","year":"2020","unstructured":"Kerzel, M., Spisak, J., Strahl, E., Wermter, S.: Neuro-genetic visuomotor architecture for robotic grasping. In: Farka\u0161, I., Masulli, P., Wermter, S. (eds.) ICANN 2020. LNCS, vol. 12397, pp. 533\u2013545. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-61616-8_43"},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Kerzel, M., Strahl, E., Magg, S., Navarro-Guerrero, N., Heinrich, S., Wermter, S.: Nico-neuro-inspired companion: A developmental humanoid robot platform for multimodal interaction. In: 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 113\u2013120. IEEE (2017)","DOI":"10.1109\/ROMAN.2017.8172289"},{"key":"29_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/978-3-319-68600-4_4","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2017","author":"M Kerzel","year":"2017","unstructured":"Kerzel, M., Wermter, S.: Neural end-to-end self-learning of visuomotor skills by environment interaction. In: Lintas, A., Rovetta, S., Verschure, P.F.M.J., Villa, A.E.P. (eds.) ICANN 2017. LNCS, vol. 10613, pp. 27\u201334. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68600-4_4"},{"key":"29_CR8","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"4","key":"29_CR9","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/s43154-020-00021-6","volume":"1","author":"K Kleeberger","year":"2020","unstructured":"Kleeberger, K., Bormann, R., Kraus, W., Huber, M.F.: A survey on learning-based robotic grasping. Curr. Robot. Rep. 1(4), 239\u2013249 (2020). https:\/\/doi.org\/10.1007\/s43154-020-00021-6","journal-title":"Curr. Robot. Rep."},{"issue":"4\u20135","key":"29_CR10","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1177\/0278364917710318","volume":"37","author":"S Levine","year":"2018","unstructured":"Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., Quillen, D.: Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. Int. J. Robot. Res. 37(4\u20135), 421\u2013436 (2018)","journal-title":"Int. J. Robot. Res."},{"key":"29_CR11","unstructured":"Matas, J., James, S., Davison, A.J.: Sim-to-real reinforcement learning for deformable object manipulation. In: Conference on Robot Learning, pp. 734\u2013743. PMLR (2018)"},{"key":"29_CR12","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32 (2019)"},{"key":"29_CR13","doi-asserted-by":"crossref","unstructured":"Pinto, L., Gupta, A.: Supersizing self-supervision: learning to grasp from 50 k tries and 700 robot hours. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 3406\u20133413. IEEE (2016)","DOI":"10.1109\/ICRA.2016.7487517"},{"key":"29_CR14","doi-asserted-by":"publisher","unstructured":"Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., Abbeel, P.: Domain randomization for transferring deep neural networks from simulation to the real world. In: 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 23\u201330 (2017). https:\/\/doi.org\/10.1109\/IROS.2017.8202133","DOI":"10.1109\/IROS.2017.8202133"}],"updated-by":[{"DOI":"10.1007\/978-3-031-15919-0_63","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T00:00:00Z","timestamp":1680134400000}}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-15919-0_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T04:26:57Z","timestamp":1680064017000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-15919-0_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031159183","9783031159190"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-15919-0_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"7 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"30 March 2023","order":2,"name":"change_date","label":"Change Date","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Correction","order":3,"name":"change_type","label":"Change Type","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"A correction has been published.","order":4,"name":"change_details","label":"Change Details","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bristol","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"561","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"255","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}