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  <title><![CDATA[PhD Defense by Maithili Patel]]></title>
  <body><![CDATA[<p><strong>Title:</strong>&nbsp;Proactivity and Personalization in Longitudinal Robotic Assistance</p><p>&nbsp;</p><p><strong>Date:&nbsp;</strong>Wednesday, July 1st, 2025</p><p><strong>Time:</strong>&nbsp;10:00am - noon ET</p><p><strong>Location:&nbsp;</strong>Klaus 1120A or <a href="https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgatech.zoom.us%2Fj%2F97630620338%3Fpwd%3DhlbbpuVL06FxEeERbagYMbV5sD4355.1&amp;data=05%7C02%7Ctm186%40gtvault.onmicrosoft.com%7Cebd4126cff2a450251ff08ded21e8118%7C482198bbae7b4b258b7a6d7f32faa083%7C1%7C0%7C639179225629362066%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&amp;sdata=n%2FRoYbr8YBjHYcRaQb5geSL8uw5g0rX1bU0x9Wc%2Fd70%3D&amp;reserved=0" title="https://gatech.zoom.us/j/97630620338?pwd=hlbbpuVL06FxEeERbagYMbV5sD4355.1">Zoom</a></p><p>&nbsp;</p><p><strong>Maithili Patel</strong></p><p>Robotics Ph.D. Student</p><p>School of Interactive Computing</p><p>Georgia Institute of Technology</p><p>&nbsp;</p><p><strong>Committee:</strong></p><ul><li data-list-item-id="e956b4d1b072332c2347bff90ac100fc2">Dr. Sonia Chernova (advisor)&nbsp;– School of Interactive Computing, Georgia Institute of Technology</li><li data-list-item-id="e25c8459f3267e0984fb040ff8c2dbbc7">Dr. Matthew Gombolay – School of Interactive Computing, Georgia Institute of Technology</li><li data-list-item-id="ee1d03dd6d31a8644b4cbf492163ab40c">Dr. Mark Riedl&nbsp;– School of Interactive Computing, Georgia Institute of Technology</li><li data-list-item-id="e745ba8bfa814ba597f26411bc5fb03c0">Dr. Roberto Martín-Martín – School of Computer Science, University&nbsp;of Texas, Austin</li><li data-list-item-id="ed3dfa0d8e9016b42e18f2470b01fa047">Dr. Jacob Andreas&nbsp;– Electrical Engineering and Computer Science, Massachusetts Institute of Technology</li></ul><p>&nbsp;</p><p><strong>Abstract:</strong></p><p>Robots are becoming increasingly capable of helping people with everyday tasks, but they remain reactive, waiting to be told what to do. This dissertation asks what it would take for a robot to become a proactive assistant that notices what users need and helps without being asked, the way a good roommate or colleague might. Achieving this requires a robot to <em>anticipate</em>&nbsp;a person's routines, <em>personalize</em>&nbsp;how it helps to match their preferences, <em>adapt</em>&nbsp;to dynamic environments and diverse tasks, and <em>interact </em>with users to gather information and minimize errors. This work builds a foundation for proactivity in such unstructured settings, beginning with a unifying formalism that connects existing efforts and defines the problem concretely. From there, it takes on the two central challenges of proactive assistance: anticipating what a user will do and personalizing how the robot helps. For anticipation, it formulates user-behavior prediction over action sequences and contributes graph-based and latent autoregressive models that forecast a person's future actions from unobtrusive observation, and a dataset&nbsp;capturing&nbsp;realistic longitudinal human behavior. For personalization, it contributes methods that generalize a user's preferences from limited feedback through abstract concept representations, and that actively elicit unspecified preferences through dialog. Finally, it studies how people respond to a proactive robot during an open-ended creative task, surfacing design insights to guide future robot design.&nbsp;Together, these contributions establish a foundation for proactive robot assistance and map out the open computational problems that future research in the field must address. The dissertation charts a course toward robots that don't just follow instructions, but take initiative in fulfilling human needs, taking on not only the physical, but also the mental burdens of everyday life.</p>]]></body>
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