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  <title><![CDATA[PhD Proposal by Bogdan Vlahov]]></title>
  <body><![CDATA[<p><strong>Title:</strong>&nbsp;Advancing Stochastic Trajectory Optimization to Achieve Human-Level Capabilities</p><p>&nbsp;</p><p><strong>Date: </strong>Monday, February 16,&nbsp;2026</p><p><strong>Time: </strong>9:30AM - 10:30AM ET</p><p><strong>Location: &nbsp;</strong>CODA 1215 Midtown</p><p><strong>Virtual Link:&nbsp;</strong>&nbsp;<a href="https://gatech.zoom.us/j/99310170679" target="_blank" title="https://gatech.zoom.us/j/99310170679">https://gatech.zoom.us/j/99310170679</a><br><strong>Meeting ID:&nbsp;</strong>993 1017 0679</p><p><strong>Passcode:&nbsp;</strong>9088308</p><p>&nbsp;</p><p>Bogdan Vlahov</p><p>Ph.D. Robotics Student</p><p>School of Mechanical Engineering</p><p>Georgia Institute of Technology</p><p>&nbsp;</p><p><strong>Committee:</strong></p><p>Dr. Evangelos Theodorou (Advisor)</p><p>School of Aerospace Engineering</p><p>Georgia Institute of Technology</p><p>&nbsp;</p><p>Dr. Glen Chou</p><p>School of Aerospace Engineering</p><p>Georgia Institute of Technology</p><p>&nbsp;</p><p>·&nbsp; Dr. Frank Dellaert</p><p>School of Interactive Computing</p><p>Georgia Institute of Technology</p><p>&nbsp;</p><p>Dr. Lu Gan</p><p>School of Aerospace Engineering</p><p>Georgia Institute of Technology</p><p>&nbsp;</p><p>Dr. Animesh Garg<br>Department of Interactive Computing</p><p>·&nbsp; Georgia Institute of Technology</p><p>·&nbsp;</p><p><strong>Abstract:</strong></p><p>Over the past several years, optimization has become a crucial component to our daily lives. With a focus on stochastic trajectory optimization, &nbsp;a variety of methods to improve the current state-of-the-art and achieve human-like capabilities in the field of autonomous off-road driving are presented. These methods include a new sampling distribution for the Model Predictive Path Integral (MPPI) algorithm, a software library for MPPI that provides control task flexibility and improved performance, and a new hierarchical planning architecture that takes into account dynamical uncertainty, perceptual uncertainty, and approximation biases. These methods have been tested in real-time hardware applications with great success. Finally, new adaptation schemes to better balance the differences between the different planners as well as machine learning techniques to improve the tuning of the cost function and controller for autonomous driving will be proposed.</p><p>&nbsp;</p>]]></body>
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