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  <title><![CDATA[Learning Perception and Control for Agile Off-Road Autonomous Driving]]></title>
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      <value><![CDATA[Learning Perception and Control for Agile Off-Road Autonomous Driving]]></value>
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      <value><![CDATA[<p>The main goal of this talk is to illustrate how machine learning can start to address some of the fundamental perceptual and control challenges involved in building intelligent robots. I&rsquo;ll start by discussing the new high speed autonomous &ldquo;rally car&rdquo; platform built in collaboration with several labs at Georgia Tech, and discuss an off-road racing task that requires impressive sensing, speed, and agility to complete. I will describe two approaches to this problem, one based on model predictive control and one based on learning deep policies that directly map images to actions. Along the way I&rsquo;ll introduce new tools from reinforcement learning, imitation learning, and online learning and show how theoretical insights help us to overcome some of the practical challenges involved in learning on a real-world platform. I will conclude by discussing ongoing work in my lab related to machine learning for robotics.<br />
&nbsp;</p>
]]></value>
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      <value><![CDATA[2018-02-02T11:00:00-05:00]]></value>
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          <item><![CDATA[Decision and Control Lab (DCL)]]></item>
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