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  <title><![CDATA[MS Defense by Joshua Kuperman]]></title>
  <body><![CDATA[<p>Joshua Kuperman<br />
Advisor: Prof. Evangelos Theodorou will defend a master’s thesis entitled,<br />
Improved Exploration for Data-Driven, Safety-Embedded Differential Dynamic Programming Using&nbsp;<br />
Tolerant Barrier States and Pontryagin Differentiable Programming<br />
On<br />
Thursday, April 20 at 10:00 a.m.<br />
Engineering, Science, and Mechanics Building G08<br />
Abstract<br />
A great challenge exists at the intersection of perception and controls – integrating the&nbsp;<br />
uncertainty present in perception-based state and obstacle estimation into safe control and&nbsp;<br />
trajectory optimization. First, we present the tolerant discrete barrier state (T-DBaS), a novel&nbsp;<br />
safety-embedding technique for trajectory optimization with enhanced exploratory capabilities. This&nbsp;<br />
approach generalizes the standard discrete barrier state (DBaS) method by accommodating temporary&nbsp;<br />
constraint violation during the optimization process while still approximating its safety&nbsp;<br />
guarantees. Towards applying T-DBaS to safety- critical autonomous robotics, we combine it with&nbsp;<br />
Differential Dynamic Programming (DDP), leading to the proposed safe trajectory optimization method&nbsp;<br />
T-DBaS-DDP, which inherits the convergence and scalability properties of the solver. Despite this,&nbsp;<br />
the tolerant barrier function parameters require tuning to reach peak performance for a wide array&nbsp;<br />
of constraints. To alleviate this requirement, we tune the T- DBaS parameters with the&nbsp;<br />
parameterized trajectory optimizer Pontryagin Differentiable Programming (PDP), proposing&nbsp;<br />
T-DBaS-PDP, an interpretable and generalizable solver for a variety of optimal control problems. In&nbsp;<br />
order to integrate perception uncertainty into safe optimal control, we learn the safety of the&nbsp;<br />
system via gaussian processes to create an interpretable, data-driven, and safety-guaranteeable&nbsp;<br />
framework. We implement this framework on differential drive and quadrotor dynamics and show its&nbsp;<br />
improvement over hand-tuned T-DBaS-DDP.<br />
Committee<br />
• &nbsp;Prof. Evangelos Theodorou – School of Aerospace Engineering (advisor)<br />
• &nbsp;Prof. Kyriakos G. Vamvoudakis – School of Aerospace Engineering<br />
• &nbsp;Prof. Patricio A. Vela – School of Electrical and Computer Engineering<br />
&nbsp;</p>
]]></body>
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]]></value>
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