{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T02:41:10Z","timestamp":1730256070041,"version":"3.28.0"},"reference-count":34,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"DOI":"10.1109\/icra57147.2024.10611657","type":"proceedings-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T17:51:05Z","timestamp":1723139465000},"page":"16970-16976","source":"Crossref","is-referenced-by-count":0,"title":["Improving Out-of-Distribution Generalization of Learned Dynamics by Learning Pseudometrics and Constraint Manifolds"],"prefix":"10.1109","author":[{"given":"Yating","family":"Lin","sequence":"first","affiliation":[{"name":"University of Michigan"}]},{"given":"Glen","family":"Chou","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}]},{"given":"Dmitry","family":"Berenson","sequence":"additional","affiliation":[{"name":"University of Michigan"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8463189"},{"key":"ref2","first-page":"465","article-title":"PILCO: A model-based and data-efficient approach to policy search","author":"Deisenroth","year":"2011","journal-title":"ICML"},{"key":"ref3","article-title":"Towards out-of-distribution generalization: A survey","volume":"abs\/2108.13624","author":"Shen","year":"2021","journal-title":"CoRR"},{"key":"ref4","first-page":"4759","article-title":"Deep reinforcement learning in a handful of trials using probabilistic dynamics models","author":"Chua","year":"2018","journal-title":"NeurIPS"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abd8170"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2972849"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48891.2023.10161001"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3068889"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CDC45484.2021.9683354"},{"key":"ref10","article-title":"CURL: contrastive unsupervised representations for reinforcement learning","author":"Laskin","year":"2020","journal-title":"ICML"},{"key":"ref11","article-title":"Equivariant Q learning in spatial action spaces","author":"Wang","year":"2021","journal-title":"CoRL"},{"key":"ref12","article-title":"Learning invariant representations for reinforcement learning without reconstruction","author":"Zhang","year":"2021","journal-title":"ICLR"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2020.XVI.101"},{"key":"ref14","article-title":"Deep lagrangian networks: Using physics as model prior for deep learning","author":"Lutter","year":"2019","journal-title":"ICLR"},{"key":"ref15","article-title":"Symplectic ode-net: Learning hamiltonian dynamics with control","author":"Zhong","year":"2020","journal-title":"ICLR"},{"key":"ref16","article-title":"Hamiltonian neural networks","author":"Greydanus","year":"2019","journal-title":"NeurIPS"},{"key":"ref17","article-title":"Learning constrained dynamics with gauss\u2019 principle adhering gaussian processes","author":"Geist","year":"2020","journal-title":"L4DC"},{"key":"ref18","article-title":"Using physics knowledge for learning rigid-body forward dynamics with gaussian process force priors","author":"Rath","year":"2021","journal-title":"CoRL"},{"key":"ref19","article-title":"Neural networks with physics-informed architectures and constraints for dynamical systems modeling","author":"Djeumou","year":"2022","journal-title":"L4DC"},{"key":"ref20","article-title":"Learning equality constraints for motion planning on manifolds","author":"Sutanto","year":"2020","journal-title":"CoRL"},{"key":"ref21","article-title":"Learning constraints from demonstrations","author":"Chou","year":"2018","journal-title":"WAFR"},{"key":"ref22","article-title":"Uncertainty-aware constraint learning for adaptive safe motion planning from demonstrations","author":"Chou","year":"2020","journal-title":"CoRL"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3148436"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2019.2955663"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9811705"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511546877"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1515\/9783110905120"},{"key":"ref28","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/3206.001.0001","volume-title":"Gaussian Processes for Machine Learning.","author":"Rasmussen","year":"2005"},{"volume-title":"General Topology.","year":"1975","author":"Kelley","key":"ref29"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.202"},{"key":"ref31","article-title":"Representation learning with contrastive predictive coding","volume":"abs\/1807.03748","author":"van den Oord","year":"2018","journal-title":"CoRR"},{"key":"ref32","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020","journal-title":"ICML"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1002\/cem.1158"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/bf01589439"}],"event":{"name":"2024 IEEE International Conference on Robotics and Automation (ICRA)","start":{"date-parts":[[2024,5,13]]},"location":"Yokohama, Japan","end":{"date-parts":[[2024,5,17]]}},"container-title":["2024 IEEE International Conference on Robotics and Automation (ICRA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10609961\/10609862\/10611657.pdf?arnumber=10611657","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T04:12:38Z","timestamp":1723349558000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10611657\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/icra57147.2024.10611657","relation":{},"subject":[],"published":{"date-parts":[[2024,5,13]]}}}