<div dir="ltr"><div dir="ltr"><div class="gmail_default" style="font-size:small"><div dir="ltr"><div><b style="font-family:verdana,sans-serif;font-size:large;color:rgb(80,0,80);background-color:rgb(207,226,243)"><span class="gmail-il">Thesis</span> <span class="gmail-il">Defense</span>: Chip Schaff, TTIC</b><br></div></div><div dir="ltr"><div><div style="color:rgb(80,0,80);font-family:arial,helvetica,sans-serif"><br></div><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><b><span style="color:black">When: </span></b><span style="color:black"> Wednesday<b>,</b> August 17th at <b style="background-color:rgb(255,255,0)">9<span style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">:</span><span style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">00 - 11:00 am CT</span></b></span></font></p><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><span style="color:black"><b style="background-color:rgb(255,255,0)"><span style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><br></span></b></span></font></p><div class="gmail_default"><font face="arial, sans-serif"><b><font color="#500050">Where: </font><font color="#000000"> </font></b><font color="#000000">Talk will be given </font><font color="#0000ff" style="font-weight:bold"><u>live, in-person</u></font><font style="color:rgb(80,0,80);font-weight:bold"> </font><font style="color:rgb(80,0,80)">at</font></font></div><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font color="#500050"> </font><font color="#000000"> TTIC, 6045 S. Kenwood Avenue</font></font></p><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif" color="#000000"> 5th Floor, Room 530<b> </b></font></p><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"> </font></p><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><span style="color:black"><b>Virtually: </b> </span><b><u><span style="color:blue"><a href="https://uchicagogroup.zoom.us/meeting/register/tJIrf-2upjwjE9TDxN4DCKHjYU2Y18wUEMui" target="_blank">attend virtually here</a></span></u></b></font></p><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"> </font></p><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><b><span style="color:black">Who: </span></b><span style="color:black"> <span class="gmail_default"> Chip Schaff</span>, TTIC</span></font><span style="font-family:Arial,sans-serif;font-size:12pt"></span></p></div><span style="color:rgb(80,0,80)"><div><font color="#000000"><br></font></div><div><font color="#000000"><b><br></b></font></div><div><div style="color:rgb(34,34,34)"><div><p class="MsoNormal" style="margin:0in 0in 8pt;font-size:11pt;text-align:justify;line-height:15.6933px;font-family:Calibri,sans-serif"><b><span class="gmail-il">Thesis</span> Title:</b> Neural Approaches to Co-Optimization in Robotics</p><p class="MsoNormal" style="margin:0in;line-height:normal"><font face="arial, sans-serif"><b>Abstract: </b>Robots and intelligent systems that sense or interact with the world are increasingly being used to automate a wide array of tasks. The ability of these systems to complete these tasks depends on a large range of technologies such as the mechanical and electrical parts that make up the physical body of the robot and its sensors, perception algorithms to perceive the environment, and planning and control algorithms to produce meaningful actions. These components have strong dependencies between them. For example, robots will perform better when their bodies admit dynamics that are well suited for the control problems that they regularly encounter, and perception systems perform better with appropriate sensor design and placement. Therefore, it is often necessary to consider the interactions between</font></p><p class="MsoNormal" style="margin:0in;line-height:normal"><font face="arial, sans-serif">these components when designing an embodied system.</font></p><p class="MsoNormal" style="margin:0in;line-height:normal"><font face="arial, sans-serif"> </font></p><p class="MsoNormal" style="margin:0in;line-height:normal"><font face="arial, sans-serif">This <span class="gmail-il">thesis</span> explores work on the task-driven optimizing of robotics systems in an end-to-end manner, simultaneously optimizing the physical components of the system with inference or control algorithms for task performance. Through the study of specific problems, such as beacon-based localization and legged locomotion, we develop a learning-based framework to co-optimize all aspects of robotics systems. In this way, this <span class="gmail-il">thesis</span> makes strides towards an efficient and automated approach to the design of robotics systems tailored to a specific application, which has the potential to both improve the performance of robotics systems and reduce the cost and barrier to entry of robot design.</font></p><p class="MsoNormal" style="margin:0in;line-height:normal"><font face="arial, sans-serif"> </font></p><p class="MsoNormal" style="margin:0in;line-height:normal"><font face="arial, sans-serif">We start by considering the problem of optimizing a beacon-based localization system directly for localization accuracy. Beacon-based localization is a popular approach in environments where GPS is unavailable, such as underwater, underground, or indoors. Designing such a system involves placing beacons throughout the environment and inferring location from sensor readings. The space of algorithms to automatically design these systems is relatively unexplored and past work often optimizes placement and inference separately. In our work, we develop a deep learning approach to optimize both beacon placement and location inference directly for localization accuracy. In simulated experiments, our approach significantly outperforms strategies that consider beacon placement and location inference</font></p><p class="MsoNormal" style="margin:0in;line-height:normal"><font face="arial, sans-serif">separately.</font></p><p class="MsoNormal" style="margin:0in;line-height:normal"><font face="arial, sans-serif"> </font></p><p class="MsoNormal" style="margin:0in;line-height:normal"><font face="arial, sans-serif">We then turn our attention to the related problem of task-driven optimization of robots and their controllers. Approaches that automate the design of robots have a long history and include several techniques such as evolutionary algorithms, trajectory optimization, and nonlinear programming. Reinforcement learning has proven successful at solving complex control problems but, at the start of our work, it was largely unexplored for co-optimization. Therefore, we start by proposing a data-efficient algorithm based on multi-task reinforcement learning. Our approach efficiently optimizes both physical design and control parameters directly for task performance by leveraging a design-conditioned controller </font><span style="font-family:arial,sans-serif">capable of generalizing over the space of physical designs. We then follow this up with an extension to allow for the optimization over discrete morphological parameters such as the number and configuration of limbs. Finally, we conclude by exploring the fabrication and deployment of optimized robots. In this work we extend our previous algorithm to </span><font face="arial, sans-serif">allow for the co-optimization of soft crawling robots, develop techniques for speeding up </font><span style="font-family:arial,sans-serif">finite element simulations, and successfully fabricate and transfer the optimized robot from </span><span style="font-family:arial,sans-serif">simulation to the real world.</span></p></div><div><font face="arial, sans-serif"><br></font></div><div><b><font face="arial, sans-serif"><span class="gmail-il">Thesis</span> Committee:</font></b></div><div><font face="arial, sans-serif"><b><a href="mailto:mwalter@ttic.edu" target="_blank">Matthew R. Walter</a></b> <b>(<span class="gmail-il">Thesis</span> Advisor)</b></font></div><div><font face="arial, sans-serif">Ayan Chakrabarti</font></div><div><font face="arial, sans-serif">Audrey Sedal</font></div><div><font face="arial, sans-serif">David McAllester</font></div><font color="#888888" face="arial, sans-serif"><div><br></div></font></div></div></span></div><br class="gmail-Apple-interchange-newline"></div><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><span style="font-family:arial,helvetica,sans-serif;font-size:x-small">Mary C. Marre</span><br></div><div><div><font face="arial, helvetica, sans-serif" size="1">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1">6045 S. Kenwood Avenue</font></i></div><div><font size="1"><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL 60637</font></i><br></font></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif" size="1">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, Aug 11, 2022 at 2:41 PM Mary Marre <<a href="mailto:mmarre@ttic.edu">mmarre@ttic.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div><div dir="ltr" style="font-size:small"><div><b style="font-family:verdana,sans-serif;font-size:large;color:rgb(80,0,80);background-color:rgb(207,226,243)"><span>Thesis</span> Defense: Chip Schaff, TTIC</b><br></div></div><div dir="ltr"><div style="font-size:small"><div style="color:rgb(80,0,80);font-family:arial,helvetica,sans-serif"><br></div><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><b><span style="color:black">When: </span></b><span style="color:black"> Wednesday<b>,</b> August 17th at <b style="background-color:rgb(255,255,0)">9<span style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">:</span><span style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">00 - 11:00 am CT</span></b></span></font></p><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><span style="color:black"><b style="background-color:rgb(255,255,0)"><span style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><br></span></b></span></font></p><div><font face="arial, sans-serif"><b><font color="#500050">Where: </font><font color="#000000"> </font></b><font color="#000000">Talk will be given </font><font color="#0000ff" style="font-weight:bold"><u>live, in-person</u></font><font style="color:rgb(80,0,80);font-weight:bold"> </font><font style="color:rgb(80,0,80)">at</font></font></div><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font color="#500050"> </font><font color="#000000"> TTIC, 6045 S. Kenwood Avenue</font></font></p><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif" color="#000000"> 5th Floor, Room 530<b> </b></font></p><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"> </font></p><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><span style="color:black"><b>Virtually: </b> </span><b><u><span style="color:blue"><a href="https://uchicagogroup.zoom.us/meeting/register/tJIrf-2upjwjE9TDxN4DCKHjYU2Y18wUEMui" target="_blank">attend virtually here</a></span></u></b></font></p><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"> </font></p><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><b><span style="color:black">Who: </span></b><span style="color:black"> <span class="gmail_default"> Chip Schaff</span>, TTIC</span></font><span style="font-family:Arial,sans-serif;font-size:12pt"></span></p></div><span style="color:rgb(80,0,80)"><div style="font-size:small"><font color="#000000"><br></font></div><div style="font-size:small"><font color="#000000"><b><br></b></font></div><div><div style="color:rgb(34,34,34)"><div><p class="MsoNormal" style="font-size:11pt;margin:0in 0in 8pt;text-align:justify;line-height:107%;font-family:Calibri,sans-serif"><b>Thesis Title:</b> Neural Approaches to Co-Optimization in
Robotics</p>
<p class="MsoNormal" style="text-align:left;margin:0in;line-height:normal"><font face="arial, sans-serif"><b>Abstract: </b>Robots and
intelligent systems that sense or interact with the world are increasingly being
used to automate a wide array of tasks. The ability of these systems to
complete these tasks depends on a large range of technologies such as the
mechanical and electrical parts that make up the physical body of the robot and
its sensors, perception algorithms to perceive the environment, and planning
and control algorithms to produce meaningful actions. These components have
strong dependencies between them. For example, robots will perform better when
their bodies admit dynamics that are well suited for the control problems that they
regularly encounter, and perception systems perform better with appropriate
sensor design and placement. Therefore, it is often necessary to consider the
interactions between</font></p>
<p class="MsoNormal" style="text-align:left;margin:0in;line-height:normal"><font face="arial, sans-serif">these
components when designing an embodied system.</font></p>
<p class="MsoNormal" style="text-align:left;margin:0in;line-height:normal"><font face="arial, sans-serif"> </font></p>
<p class="MsoNormal" style="text-align:left;margin:0in;line-height:normal"><font face="arial, sans-serif">This thesis
explores work on the task-driven optimizing of robotics systems in an end-to-end
manner, simultaneously optimizing the physical components of the system with
inference or control algorithms for task performance. Through the study of
specific problems, such as beacon-based localization and legged locomotion, we
develop a learning-based framework to co-optimize all aspects of robotics
systems. In this way, this thesis makes strides towards an efficient and
automated approach to the design of robotics systems tailored to a specific
application, which has the potential to both improve the performance of
robotics systems and reduce the cost and barrier to entry of robot design.</font></p>
<p class="MsoNormal" style="text-align:left;margin:0in;line-height:normal"><font face="arial, sans-serif"> </font></p>
<p class="MsoNormal" style="text-align:left;margin:0in;line-height:normal"><font face="arial, sans-serif">We start by
considering the problem of optimizing a beacon-based localization system directly
for localization accuracy. Beacon-based localization is a popular approach in environments
where GPS is unavailable, such as underwater, underground, or indoors. Designing
such a system involves placing beacons throughout the environment and inferring
location from sensor readings. The space of algorithms to automatically design
these systems is relatively unexplored and past work often optimizes placement
and inference separately. In our work, we develop a deep learning approach to
optimize both beacon placement and location inference directly for localization
accuracy. In simulated experiments, our approach significantly outperforms
strategies that consider beacon placement and location inference</font></p>
<p class="MsoNormal" style="text-align:left;margin:0in;line-height:normal"><font face="arial, sans-serif">separately.</font></p>
<p class="MsoNormal" style="text-align:left;margin:0in;line-height:normal"><font face="arial, sans-serif"> </font></p>
<p class="MsoNormal" style="text-align:left;margin:0in;line-height:normal"><font face="arial, sans-serif">We then turn
our attention to the related problem of task-driven optimization of robots and
their controllers. Approaches that automate the design of robots have a long
history and include several techniques such as evolutionary algorithms,
trajectory optimization, and nonlinear programming. Reinforcement learning has
proven successful at solving complex control problems but, at the start of our
work, it was largely unexplored for co-optimization. Therefore, we start by
proposing a data-efficient algorithm based on multi-task reinforcement
learning. Our approach efficiently optimizes both physical design and control parameters
directly for task performance by leveraging a design-conditioned controller </font><span style="font-family:arial,sans-serif">capable of
generalizing over the space of physical designs. We then follow this up with an
extension to allow for the optimization over discrete morphological parameters
such as the number and configuration of limbs. Finally, we conclude by
exploring the fabrication and deployment of optimized robots. In this work we
extend our previous algorithm to </span><font face="arial, sans-serif">allow for the
co-optimization of soft crawling robots, develop techniques for speeding up </font><span style="font-family:arial,sans-serif">finite element
simulations, and successfully fabricate and transfer the optimized robot from </span><span style="font-family:arial,sans-serif">simulation to
the real world.</span></p></div><div><font face="arial, sans-serif"><br></font></div><div><b><font face="arial, sans-serif">Thesis Committee:</font></b></div><div><font face="arial, sans-serif"><b><a href="mailto:mwalter@ttic.edu" target="_blank">Matthew R. Walter</a></b> <b>(Thesis Advisor)</b></font></div><div><font face="arial, sans-serif">Ayan Chakrabarti</font></div><div><font face="arial, sans-serif">Audrey Sedal</font></div><div><font face="arial, sans-serif">David McAllester</font></div><font color="#888888" face="arial, sans-serif"><div><br></div></font></div><br><br></div><div style="font-size:small"><br></div><div style="font-size:small"><br></div></span></div></div><div><div dir="ltr"><div dir="ltr"><div><span style="font-family:arial,helvetica,sans-serif;font-size:x-small">Mary C. Marre</span><br></div><div><div><font face="arial, helvetica, sans-serif" size="1">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1">6045 S. Kenwood Avenue</font></i></div><div><font size="1"><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL 60637</font></i><br></font></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif" size="1">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div></div>
</blockquote></div></div>