{"691891":{"#nid":"691891","#data":{"type":"event","title":"PhD Proposal by Utkarsh A. Mishra","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E Compositional Generative Modeling for Robotic Planning and Control\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E Tuesday, September 1st, 2026\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E 9:00 AM to 10:30 AM ET\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EIn-person Location:\u003C\/strong\u003E\u0026nbsp;CODA C0903 Midtown\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EZoom:\u003C\/strong\u003E\u0026nbsp;\u003Ca href=\u0022https:\/\/nam12.safelinks.protection.outlook.com\/?url=https%3A%2F%2Fgatech.zoom.us%2Fj%2F97270969399%3Fpwd%3DXTTOoLyDVa6w8xOKXcTYLmq6I5YueA.1\u0026amp;data=05%7C02%7Cannouncements%40grad.gatech.edu%7C3e83054f9331437fb8a508df01e85fc0%7C482198bbae7b4b258b7a6d7f32faa083%7C1%7C0%7C639231769676923864%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C\u0026amp;sdata=sB%2FsJyHmMHUtuMMzbj6Ie%2F0kJb%2FVRNj6Z1oe8ro72gI%3D\u0026amp;reserved=0\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/97270969399?pwd=XTTOoLyDVa6w8xOKXcTYLmq6I5YueA.1\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EUtkarsh Aashu Mishra\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EPh.D. Student\u003Cbr\u003EInstitute for Robotics \u0026amp; Intelligent Machines\u0026nbsp;\u003Cbr\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr. Yongxin Chen (advisor) -- Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Danfei Xu (advisor) -- School of Interactive Computing, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Harish Ravichandar -- School of Interactive Computing, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Leslie Pack Kaelbling -- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Yilun Du -- School of Engineering and Applied Sciences, Harvard University\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003ELong-horizon manipulation requires planning for many interdependent actions in sequence to satisfy a goal condition. Current robot-learning methods are limited along three axes: (a) Data: whole-task demonstrations are combinatorially expensive, so monolithic policies do not scale. (b) Inter-step dependencies: a locally optimal early action can make a later step infeasible, and errors compound. (c) Partial observability: an open-world scene is not fully known at planning time. The first part of this proposal develops a long-horizon planning framework that learns distributions of short-horizon behaviors using generative models and composes them at inference to generate long-horizon plans. The compositional generative modeling reasons about inter-step dependencies and goal reaching without any whole-task demonstrations. The second part extends this framework toward open-world planning along two directions: (i) it explores the compositional generalization of short-horizon models trained on a diverse set of real-world, manipulation-rich behaviors, and (ii) it adds a verification harness, an independent symbolic-geometric verifier that checks whether a proposed plan is geometrically executable before execution. Across both axes, the objective is to build a reliable framework that can replan and sufficiently generalize across behaviors and planning horizon: a composed plan should be a plausible real-world future prediction while satisfying the long-horizon goal under inter-step constraints.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ECompositional Generative Modeling for Robotic Planning and Control\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Compositional Generative Modeling for Robotic Planning and Control"}],"uid":"27707","created_gmt":"2026-08-24 14:51:23","changed_gmt":"2026-08-24 14:52:40","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-09-01T09:00:00-04:00","event_time_end":"2026-09-01T10:30:11-04:00","event_time_end_last":"2026-09-01T10:30:11-04:00","gmt_time_start":"2026-09-01 13:00:00","gmt_time_end":"2026-09-01 14:30:11","gmt_time_end_last":"2026-09-01 14:30:11","rrule":null,"timezone":"America\/New_York"},"location":"CODA C0903 Midtown","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}