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<title>Gray Cortright Thomas</title>
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      <h1 class="title">Gray Cortright Thomas</h1>
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        Research Investigator at the University of Michigan in direct human control of physically interactive robots.
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     <h1>Bio</h1>
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Gray C. Thomas is a Research Investigator at the University of Michigan in the Robotics Department, working with Dr. <a href="https://gregg.engin.umich.edu/">Robert Gregg</a>. He studies direct human control of physically interactive robots through a combination of strength amplification control, human modeling and estimation, and high-bandwidth design. He earned his 2019 Ph.D. in Mechanical Engineering from the University of Texas at Austin, where he worked with Professor Luis Sentis on the topics of multi-contact impedance control for humanoid robots, system identification for robust control, and series elastic actuation. Prior to his Ph.D., he earned his B.S. in Engineering:Robotics at Olin College of Engineering in 2012. He was the recipient of the NASA Space Technology Research Fellowship and Olin Full-Tuition Scholarship. Other research interests include force-feedback, series-elastic actuation, and robust control.
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        <!-- I received a B.S. from <a href="http://www.olin.edu/">Olin College of Engineering</a>, Needham Massachusetts, in 2012—designing my own major in robotics engineering. In the summer of 2012 I joined the <a href="https://www.ihmc.us/">Florida Institute for Human and Machine Cognition</a>, and participated in the <a href="http://archive.darpa.mil/roboticschallenge/">DARPA virtual robotics challenge</a>—which we won! in the fall of 2013, I joined the <a href="http://sites.utexas.edu/hcrl/">Human Centered Robotics Lab</a> at the University of Texas at Austin, eventually obtaining a Ph.D. in Mechanical Engineering in 2019. Between 2015 and 2019 I was funded through a NASA Space Technology Research Fellowship, collaborating with <a href="https://github.com/NASA-JSC-Robotics">Johnson Space Center's Robotics Team</a> on the control of compliant humanoid robots. Along the way, I discovered that the same mechanical impedance shaping mathematics I had been applying to humanoid robots could also be applied to strength amplifying exoskeletons. In 2018, I started collaborating with <a href="https://apptronik.com/">Apptronik Inc.</a> (an HCRL spinoff) to apply these techniques to the Sagitarius lower body exoskeleton.</p> -->

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His long-term research goal is to pursue  physically interactive robots through a combination of modeling and estimation, applied control, and mechatronic design. This includes exoskeletons that make people stronger by measuring and amplifying human power, powered prosthetic legs that the human controls, and industrial systems designed for heavy manipulation. He envisions these robots assisting humans in every day life and enabling new industries by seamlessly augmenting human motions and forces. For example, an elderly person might wear an exoskeleton that magnifies her knee strength so she can safely descend stairs; a person with a missing limb might hike their hip in order to signal their robot leg to clear a Lego castle; and a stonemason might use block sizes that would be impractically massive without the strength provided by their giant robot suit. Today this vision is hindered by a suite of engineering challenges in design, sensing, estimation, human modeling, control, and system integration. His research program in direct human control is working to overcome these challenges using strength amplification, human modeling and estimation, and high-bandwidth design. Strength amplification is directly controlling the robot using the wearer's forces to make it a trustworthy assistive tool like power-steering on a car. Using the sensors on a prosthesis to track unusual walking patterns (which the wearer can control) and mapping these patterns to something like push off power or swing height (which the wearer can't control but wants to) is one example of his research in human modeling and estimation. And he plans to build interface pads and sockets that distribute machine power over a large area of human tissue in order to move bones quickly and avoid the delay of deforming soft tissue first as a key part of my research in high-bandwidth design.</p>


<h1> IEEE Style </h1>
<p>
{Gray Cortright Thomas} (Member, IEEE) received the B.S. degree in Engineering:Robotics from Olin College of Engineering, Needham, MA, USA, in 2012, and the Ph.D. degree in mechanical engineering from the University of Texas at Austin, Austin, TX, USA, in 2019. In 2019, he began a postdoctoral fellowship at the University of Michigan, and is now a Research Investigator with the Department of Robotics. His research interests include direct human control of physically interactive robots, system identification, applied state estimation, robust control, force feedback, and series elastic actuation.
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       <!--    I am currently a postdoctoral research fellow at the University of Michigan, Ann Arbor, working with Drs. <a href="https://gregg.engin.umich.edu/">Robert D. Gregg</a> and <a href="https://neurobionics.robotics.umich.edu/profiles/elliott-rouse/">Elliott Rouse</a> in the <a href="https://web.eecs.umich.edu/locolab/team.html">Locomotor Control Systems</a> and <a href="https://neurobionics.robotics.umich.edu/">Neurobionics Laboratory</a>. My research continues to apply my background in estimation and control to wearable robots. Through studying high bandwidth wearable feedback systems, I hope to surpass the perceptual threshold of strength amplification behaviors, and create a class of devices that can function as seamless extensions of the human neuomuscular system.
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