{"674044":{"#nid":"674044","#data":{"type":"event","title":"Phd Defense by Joanne Truong","body":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003ETitle\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E\u003Cspan\u003E: Sim2Robot: Training Robots for the Real-World with Imperfect Simulators\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u0026nbsp;\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003EDate\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E\u003Cspan\u003E: Friday, April 19th, 2024\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003ETime\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E\u003Cspan\u003E: 3:00 PM \u2013 5:00 PM EST\u0026nbsp; \/ 12:00 PM \u2013 2:00 PM PST\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003ELocation\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E\u003Cspan\u003E: \u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/2155716988?pwd=eXNZRGVqbStOSm9OU29NVU9ROVpwUT09\u0022\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EZoom\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/a\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003EJoanne Truong\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ERobotics Ph.D. Student\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ESchool of Interactive Computing\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EGeorgia Institute of Technology\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u0026nbsp;\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003ECommittee\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E\u003Cspan\u003E:\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDr. Sonia Chernova (Advisor) \u2013 School of Interactive Computing, Georgia Institute of Technology\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDr. Dhruv Batra (Advisor) \u2013 School of Interactive Computing, Georgia Institute of Technology\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDr. Jie Tan \u2013 School of Interactive Computing, Georgia Institute of Technology\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDr. Vladlen Koltun \u2013 Distinguished Scientist, Apple\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDr. Vincent Vanhoucke \u2013\u0026nbsp; Distinguished Scientist, Google\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDr. Ken Goldberg \u2013\u0026nbsp; Industrial Engineering and Operations Research Department, University of California Berkeley\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u0026nbsp;\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003EAbstract\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E\u003Cspan\u003E:\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EThe goal of Artificial Intelligence is to \u201cconstruct useful intelligent systems\u201d, such as mobile robots, to assist in our day-to-day lives. For these mobile robotic assistants to be useful in the real-world, they must skillfully navigate complex environments (e.g., delivering packages from one building to another). However, training robots in the real-world can be slow, dangerous, expensive, and difficult to reproduce. Thus, one paradigm in robot learning is to leverage simulation for training robots (where gathering experience is scalable, safe, cheap, and reproducible) before being deployed in the real world. However, no simulator is perfect; AI systems learn to \u201ccheat\u201d by exploiting imperfections. Thus, how can we train robots in imperfect simulators while ensuring that the learned skills generalize to reality?\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EIn this thesis, we will argue that simulators need not be perfect to be useful; they don\u2019t need to model everything about the world, only what\u2019s necessary for generalization. We present 1) Sim2Real Correlation Coefficient (SRCC), a formal approach for simulator design by revealing the degree to which simulation parameters allow agents to cheat. SRCC optimization enables researchers to use simulation for evaluation with confidence that the performance of different models will generalize to real robots. 2) Bi-directional Domain Adaptation (BDA), and Kinematic-to-Dynamic Transfer (Kin2Dyn), sample-efficient methods for reducing the sim2real gap. BDA and Kin2Dyn improve robot learning and generalization to the real-world by utilizing abstracted physics and simple adaptation models learned from small amounts of real-world data. 3) IndoorSim-to-OutdoorReal, an end-to-end learned approach that enables visual navigation in out-of-distribution environments zero-shot. We show that simulators can be used for real-world transfer without having to apriori design and model the deployment scenario. 4) Implicit Map Cross Modal Attention (Implicit MapCMA), a vision and language navigation model that utilizes structured implicit maps for navigating in an environment over time. Agents that leverage implicit maps are able to more effectively follow the ground-truth path indicated by the language instruction.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ESim2Robot: Training Robots for the Real-World with Imperfect Simulators\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Sim2Robot: Training Robots for the Real-World with Imperfect Simulators"}],"uid":"27707","created_gmt":"2024-04-08 17:36:14","changed_gmt":"2024-04-08 17:36:49","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-04-19T15:00:00-04:00","event_time_end":"2024-04-19T17:00:00-04:00","event_time_end_last":"2024-04-19T17:00:00-04:00","gmt_time_start":"2024-04-19 19:00:00","gmt_time_end":"2024-04-19 21:00:00","gmt_time_end_last":"2024-04-19 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Zoom","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}