{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:04:44Z","timestamp":1764842684361,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031210891"},{"type":"electronic","value":"9783031210907"}],"license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-21090-7_10","type":"book-chapter","created":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T18:11:35Z","timestamp":1671041495000},"page":"149-169","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Sample-Efficient Safety Assurances Using Conformal Prediction"],"prefix":"10.1007","author":[{"given":"Rachel","family":"Luo","sequence":"first","affiliation":[]},{"given":"Shengjia","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Jonathan","family":"Kuck","sequence":"additional","affiliation":[]},{"given":"Boris","family":"Ivanovic","sequence":"additional","affiliation":[]},{"given":"Silvio","family":"Savarese","sequence":"additional","affiliation":[]},{"given":"Edward","family":"Schmerling","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Pavone","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"10_CR1","unstructured":"Lyft motion prediction dataset. https:\/\/www.kaggle.com\/c\/lyft-motion-prediction-autonomous-vehicles\/data (2020)."},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuscenes: a multimodal dataset for autonomous driving (2019). arXiv:1903.11027","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Cai, F., Koutsoukos, X.: Real-time out-of-distribution detection in learning-enabled cyber-physical systems. In: 2020 ACM\/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), pp. 174\u2013183 (2020)","DOI":"10.1109\/ICCPS48487.2020.00024"},{"issue":"5","key":"10_CR4","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1109\/TAC.2006.875041","volume":"51","author":"G Calafiore","year":"2006","unstructured":"Calafiore, G., Campi, M.: The scenario approach to robust control design. IEEE Trans. Autom. Control 51(5), 742\u2013753 (2006)","journal-title":"IEEE Trans. Autom. Control"},{"key":"10_CR5","unstructured":"Chen, Y., Rosolia, U., Fan, C., Ames, A., Murray, R.: Reactive motion planning with probabilistic safety guarantees. In: Conference on Robot Learning (2020)"},{"issue":"1","key":"10_CR6","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TASE.2016.2600527","volume":"15","author":"N Correll","year":"2016","unstructured":"Correll, N., Bekris, K.E., Berenson, D., Brock, O., Causo, A., Hauser, K., Okada, K., Rodriguez, A., Romano, J.M., Wurman, P.R.: Analysis and observations from the first amazon picking challenge. IEEE Trans. Autom. Sci. Eng. 15(1), 172\u2013188 (2016)","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"10_CR7","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.robot.2014.12.015","volume":"68","author":"D Crestani","year":"2015","unstructured":"Crestani, D., Godary-Dejean, K., Lapierre, L.: Enhancing fault tolerance of autonomous mobile robots. Robot. Auton. Syst. 68, 140\u2013155 (2015)","journal-title":"Robot. Auton. Syst."},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Ding, S.X.: Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms and Tools, pp. 3\u201311. Springer, London (2013)","DOI":"10.1007\/978-1-4471-4799-2_1"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Eppner, C., H\u00f6fer, S., Jonschkowski, R., Mart\u00edn-Mart\u00edn, R., Sieverling, A., Wall, V., Brock, O.: Lessons from the amazon picking challenge: Four aspects of building robotic systems. In: Robotics: Science and Systems (2016)","DOI":"10.24963\/ijcai.2017\/676"},{"key":"10_CR10","unstructured":"Feldman, S., Bates, S., Romano, Y.: Improving conditional coverage via orthogonal quantile regression (2021). arXiv:2106.00394"},{"issue":"20","key":"10_CR11","doi-asserted-by":"publisher","first-page":"5273","DOI":"10.1080\/01431160903130937","volume":"30","author":"GM Foody","year":"2009","unstructured":"Foody, G.M.: Sample size determination for image classification accuracy assessment and comparison. Int. J. Remote Sens. 30(20), 5273\u20135291 (2009)","journal-title":"Int. J. Remote Sens."},{"issue":"2","key":"10_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2202\/1544-6115.1385","volume":"7","author":"A Gammerman","year":"2008","unstructured":"Gammerman, A., Nouretdinov, I., Burford, B., Chervonenkis, A., Vovk, V., Luo, Z.: Clinical mass spectrometry proteomic diagnosis by conformal predictors. Stat. Appl. Genet. Mol. Biol. 7(2), 1\u201312 (2008)","journal-title":"Stat. Appl. Genet. Mol. Biol."},{"issue":"27","key":"10_CR13","first-page":"260","volume":"48","author":"F Harirchi","year":"2015","unstructured":"Harirchi, F., Ozay, N.: Model invalidation for switched affine systems with applications to fault and anomaly detection. Anal. Design Hybrid Syst. (ADHS) 48(27), 260\u2013266 (2015)","journal-title":"Anal. Design Hybrid Syst. (ADHS)"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Harirchi, F., Ozay, N.: Guaranteed model-based fault detection in cyber-physical systems: a model invalidation approach (2017). arXiv:1609.05921","DOI":"10.1016\/j.automatica.2018.03.040"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Hernandez, C., Bharatheesha, M., Ko, W., Gaiser, H., Tan, J., van Deurzen, K., de Vries, M., Van Mil, B., van Egmond, J., Burger, R., et al.: Team delft\u2019s robot winner of the amazon picking challenge 2016. In: Robot World Cup, pp. 613\u2013624. Springer (2016)","DOI":"10.1007\/978-3-319-68792-6_51"},{"issue":"1","key":"10_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3146389","volume":"51","author":"E Khalastchi","year":"2018","unstructured":"Khalastchi, E., Kalech, M.: On fault detection and diagnosis in robotic systems. ACM Comput. Surv. (CSUR) 51(1), 1\u201324 (2018)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"von Luxburg, U., Sch\u00f6lkopf, B.: Statistical learning theory: models, concepts, and results. In: Gabbay, D.M., Hartmann, S., Woods, J. (eds.) Inductive Logic, Handbook of the History of Logic, vol. 10, pp. 651\u2013706. North-Holland (2011)","DOI":"10.1016\/B978-0-444-52936-7.50016-1"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Mahler, J., Liang, J., Niyaz, S., Laskey, M., Doan, R., Liu, X., Ojea, J.A., Goldberg, K.: Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics. Robotics: Science and Systems (RSS) (2017)","DOI":"10.15607\/RSS.2017.XIII.058"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Mahler, J., Matl, M., Liu, X., Li, A., Gealy, D., Goldberg, K.: Dex-net 3.0: Computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning (2017). arXiv:1709.06670","DOI":"10.1109\/ICRA.2018.8460887"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Mahler, J., Matl, M., Satish, V., Danielczuk, M., DeRose, B., McKinley, S., Goldberg, K.: Learning ambidextrous robot grasping policies. Sci. Robot. 4(26), eaau4984 (2019)","DOI":"10.1126\/scirobotics.aau4984"},{"issue":"8","key":"10_CR21","doi-asserted-by":"publisher","first-page":"3167","DOI":"10.1109\/TIE.2011.2167110","volume":"59","author":"R Muradore","year":"2011","unstructured":"Muradore, R., Fiorini, P.: A pls-based statistical approach for fault detection and isolation of robotic manipulators. IEEE Trans. Ind. Electron. 59(8), 3167\u20133175 (2011)","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"2","key":"10_CR22","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1016\/j.neuroimage.2010.05.023","volume":"56","author":"I Nouretdinov","year":"2011","unstructured":"Nouretdinov, I., Costafreda, S.G., Gammerman, A., Chervonenkis, A., Vovk, V., Vapnik, V., Fu, C.H.: Machine learning classification with confidence: application of transductive conformal predictors to mri-based diagnostic and prognostic markers in depression. NeuroImage 56(2), 809\u2013813 (2011)","journal-title":"NeuroImage"},{"issue":"5","key":"10_CR23","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1016\/S0967-0661(97)00049-X","volume":"5","author":"R Patton","year":"1997","unstructured":"Patton, R., Chen, J.: Observer-based fault detection and isolation: Robustness and applications. Control Eng. Pract. 5(5), 671\u2013682 (1997)","journal-title":"Control Eng. Pract."},{"key":"10_CR24","unstructured":"Perdomo, J., Zrnic, T., Mendler-D\u00fcnner, C., Hardt, M.: Performative prediction. In: International Conference on Machine Learning, pp. 7599\u20137609. PMLR (2020)"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Salzmann, T., Ivanovic, B., Chakravarty, P., Pavone, M.: Trajectron++: dynamically-feasible trajectory forecasting with heterogeneous data (2020)","DOI":"10.1007\/978-3-030-58523-5_40"},{"key":"10_CR26","unstructured":"Shafer, G., Vovk, V.: A tutorial on conformal prediction. J. Mach. Learn. Res. (JMLR). https:\/\/jmlr.csail.mit.edu\/papers\/volume9\/shafer08a\/shafer08a.pdf (2008)"},{"key":"10_CR27","unstructured":"Tibshirani, R.J., Barber, R.F., Cand\u00e8s, E.J., Ramdas, A.: Conformal prediction under covariate shift (2019). arXiv:1904.06019"},{"issue":"2","key":"10_CR28","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1109\/70.681254","volume":"14","author":"AT Vemuri","year":"1998","unstructured":"Vemuri, A.T., Polycarpou, M.M., Diakourtis, S.A.: Neural network based fault detection in robotic manipulators. IEEE Trans. Robot. Autom. 14(2), 342\u2013348 (1998)","journal-title":"IEEE Trans. Robot. Autom."},{"issue":"5","key":"10_CR29","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1016\/0045-7906(94)90035-3","volume":"20","author":"ML Visinsky","year":"1994","unstructured":"Visinsky, M.L., Cavallaro, J.R., Walker, I.D.: Expert system framework for fault detection and fault tolerance in robotics. Comput. & Electr. Eng. 20(5), 421\u2013435 (1994)","journal-title":"Comput. & Electr. Eng."},{"issue":"2","key":"10_CR30","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/0951-8320(94)90132-5","volume":"46","author":"ML Visinsky","year":"1994","unstructured":"Visinsky, M.L., Cavallaro, J.R., Walker, I.D.: Robotic fault detection and fault tolerance: a survey. Reliab. Eng. & Syst. Safety 46(2), 139\u2013158 (1994)","journal-title":"Reliab. Eng. & Syst. Safety"},{"issue":"4","key":"10_CR31","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1109\/70.406930","volume":"11","author":"ML Visinsky","year":"1995","unstructured":"Visinsky, M.L., Cavallaro, J.R., Walker, I.D.: A dynamic fault tolerance framework for remote robots. IEEE Trans. Robot. Autom. 11(4), 477\u2013490 (1995)","journal-title":"IEEE Trans. Robot. Autom."},{"key":"10_CR32","unstructured":"Vovk, V., Gammerman, A., Shafer, G.: Algorithmic learning in a random world (2005)"},{"key":"10_CR33","unstructured":"Vovk, V., Lindsay, D., Nouretdinov, I.: Mondrian confidence machine (2003)"},{"key":"10_CR34","unstructured":"Yu, K.T., Fazeli, N., Chavan-Dafle, N., Taylor, O., Donlon, E., Lankenau, G.D., Rodriguez, A.: A summary of team mit\u2019s approach to the amazon picking challenge 2015 (2016). arXiv:1604.03639"},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Zeng, A., Yu, K.T., Song, S., Suo, D., Walker, E., Rodriguez, A., Xiao, J.: Multi-view self-supervised deep learning for 6d pose estimation in the amazon picking challenge. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 1386\u20131383. IEEE (2017)","DOI":"10.1109\/ICRA.2017.7989165"}],"container-title":["Springer Proceedings in Advanced Robotics","Algorithmic Foundations of Robotics XV"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21090-7_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T20:55:18Z","timestamp":1679086518000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21090-7_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,15]]},"ISBN":["9783031210891","9783031210907"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21090-7_10","relation":{},"ISSN":["2511-1256","2511-1264"],"issn-type":[{"type":"print","value":"2511-1256"},{"type":"electronic","value":"2511-1264"}],"subject":[],"published":{"date-parts":[[2022,12,15]]},"assertion":[{"value":"15 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WAFR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on the Algorithmic Foundations of Robotics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":", MD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","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":"22 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wafr2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wafr2022.github.io","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}