update Jiayuan's talk

h2r · Apr 4, 2024 · 44d5548 · 44d5548
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diff --git a/_speakers/5_jiayuanmao.md b/_speakers/5_jiayuanmao.md
@@ -21,4 +21,4 @@ img: jiayuan.jpeg
 
 <!-- Whatever you write below will show up as the speaker's bio -->
 
-__TBD__
+Jiayuan Mao is a Ph.D. student at MIT, advised by Professors Josh Tenenbaum and Leslie Kaelbling. Her research aim is to build machines that can continually learn concepts (e.g., properties, relations, rules, and skills) from their experiences and apply them for reasoning and planning in the physical world. Her research topics include visual reasoning, robotic manipulation, scene and activity understanding, and language acquisition.
diff --git a/assets/abstracts/jiayuanmao.txt b/assets/abstracts/jiayuanmao.txt
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+In this talk, I will discuss an integrated learning and planning approach for flexible and general robotic manipulators. I will primarily focus on the technical idea of leveraging compositional abstract representations built on top of two important spatio-temporal structures: factorization and sparsity structures in state representations (the physical state can be represented as a collection of object states and their relational configurations), and hierarchical structures in plans (a high-level goal can be decomposed into subgoals). I will talk about the design of such representations and the overall architecture in the context of robot manipulation, present methods for learning them automatically from data, and showcase various types of generalization enabled by such a framework.
diff --git a/index.md b/index.md
@@ -42,13 +42,13 @@ Brown Robotics Talks consists of BigAI talks and lab talks ([CIT](https://www.go
     <td>Felix Yanwei Wang</td>
   </tr>
   <tr>
-    <td>05/10</td>
+    <td>03/08</td>
     <td><b>Towards Composable Scene Representations in Robotics and Vision</b> [<a href='assets/abstracts/ondrejbiza.txt' target="_blank">abstract</a>][<a href='https://brown.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=b0a1556d-bb90-4fca-a239-b12d0130a9bb' target='_blank'>recording</a>]</td>
     <td>Ondrej Biza</td>
   </tr>
   <tr>
     <td>04/19</td>
-    <td><b>TBD</b></td>
+    <td><b>Building General-Purpose Robots with Integrated Learning and Planning</b> [<a href='assets/abstracts/jiayuanmao.txt' target="_blank">abstract</a>]</td>
     <td>Jiayuan Mao</td>
   </tr>
   <tr>
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		<!-- Whatever you write below will show up as the speaker's bio -->

		__TBD__
		Jiayuan Mao is a Ph.D. student at MIT, advised by Professors Josh Tenenbaum and Leslie Kaelbling. Her research aim is to build machines that can continually learn concepts (e.g., properties, relations, rules, and skills) from their experiences and apply them for reasoning and planning in the physical world. Her research topics include visual reasoning, robotic manipulation, scene and activity understanding, and language acquisition.
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		In this talk, I will discuss an integrated learning and planning approach for flexible and general robotic manipulators. I will primarily focus on the technical idea of leveraging compositional abstract representations built on top of two important spatio-temporal structures: factorization and sparsity structures in state representations (the physical state can be represented as a collection of object states and their relational configurations), and hierarchical structures in plans (a high-level goal can be decomposed into subgoals). I will talk about the design of such representations and the overall architecture in the context of robot manipulation, present methods for learning them automatically from data, and showcase various types of generalization enabled by such a framework.