<div dir="ltr">A gentle reminder that the talk would be tomorrow (Tuesday).<div><br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Sat, Apr 8, 2017 at 12:00 PM,  <span dir="ltr"><<a href="mailto:ai-seminar-announce-request@cs.cmu.edu" target="_blank">ai-seminar-announce-request@cs.cmu.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Send ai-seminar-announce mailing list submissions to<br>
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Today's Topics:<br>
<br>
   1.  AI Lunch -- Jiaji Zhou -- April 11 (Adams Wei Yu)<br>
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Message: 1<br>
Date: Fri, 7 Apr 2017 12:00:17 -0400<br>
From: Adams Wei Yu <<a href="mailto:weiyu@cs.cmu.edu">weiyu@cs.cmu.edu</a>><br>
To: <a href="mailto:ai-seminar-announce@cs.cmu.edu">ai-seminar-announce@cs.cmu.edu</a><br>
Subject: [AI Seminar] AI Lunch -- Jiaji Zhou -- April 11<br>
Message-ID:<br>
        <<a href="mailto:CABzq7epyCMdGWmRt9r0%2Bz%2B%2B9Obb7eamM-Wb1YCHJ_Q%2BO1Y9EqQ@mail.gmail.com">CABzq7epyCMdGWmRt9r0+z++<wbr>9Obb7eamM-Wb1YCHJ_Q+O1Y9EqQ@<wbr>mail.gmail.com</a>><br>
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<br>
Dear faculty and students,<br>
<br>
We look forward to seeing you Next Tuesday, April 11, at noon in NSH 3305<br>
for AI lunch. To learn more about the seminar and lunch, please visit the AI<br>
 Lunch webpage <<a href="http://www.cs.cmu.edu/~aiseminar/" rel="noreferrer" target="_blank">http://www.cs.cmu.edu/~<wbr>aiseminar/</a>>.<br>
<br>
On Tuesday, Jiaji Zhou <<a href="http://www.cs.cmu.edu/~jiajiz/" rel="noreferrer" target="_blank">http://www.cs.cmu.edu/~<wbr>jiajiz/</a>> will give the<br>
following talk:<br>
<br>
Title: Exploiting Task Mechanics for Contact-rich Robotic Manipulation:<br>
Mechanics Model Learning, Uncertainty Reduction and Control.<br>
<br>
Abstract:<br>
<br>
Effective robotic manipulation requires an understanding of the underlying<br>
physical processes. In this talk, I will start with first principle guided<br>
mechanics model learning for planar sliding. The model is further extended<br>
for simulating generic quasi-static planar contact problems including<br>
pushing and grasping. The second part of the talk is on tree-based grasping<br>
policy synthesis for uncertainty reduction given encoder feedback. If time<br>
permits, I will end the talk with some recent work on control synthesis and<br>
trajectory planning for pushing.<br>
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Subject: Digest Footer<br>
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End of ai-seminar-announce Digest, Vol 73, Issue 4<br>
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