Justin W. Hart
hart@cs.utexas.edu
Assistant Professor of Practice
Assistant Director of Texas Robotics
Department of Computer Science, UT Austin
CV [PDF]
I am an assistant professor of practice teaching the Autonomous Robots stream of the Freshman Research Initiative at UT Austin. I work under the supervision of Professor Peter Stone in the Learning Agents Research Group in the Department of Computer Science. I co-lead the Building-Wide Intelligence Project, and supervise the UT Austin Villa @ Home RoboCup@Home team. I research artificial intelligence and human-robot interaction. Significant to my work at UT is the development of comprehensive systems and enabling technologies for general purpose service robots. Topics include the construction of architectures for long-term autonomy, knowledge representation, semantic mapping as it relates to both planning and scene understanding, and autonomous human-robot interaction.
I completed my doctoral thesis under the supervision of Professor Brian Scassellati in the Department of Computer Science at Yale University in 2014. An important research interest of mine is the emulation of the developmental process by which infants develop self-awareness, as modeled in robots. My dissertation topic was the related topic of Robot Self-Modeling, by which robots learn about their bodies and senses through experience. I also worked on problems in human-robot interaction and contributed to the mechanical design and machining of our humanoid robot, Nico.
Previously I worked as a postdoctoral fellow in the Department of Mechanical Engineering at the University of British Columbia under the supervision of Elizabeth Croft. There, I researched human-robot collaborative manufacturing, and, in particular, the prediction of reach trajectories; which would allow for robots to predict human behavior in order to improve the speed and fluidity of physical collaboration.
My core research interests are artificial intelligence, human-robot interaction, and robot self-modeling. In particular, I am interested in themes in which we model human intelligence, leverage knowledge of human behavior, or take inspiration from human behavior. Additionally, I am interested in themes which I believe are likely to shape the direction of robotics and move robots into homes, workspaces, and public places, such as service robots. The dual goals of my research are to better understand human intelligence and to push the fields of artificial intelligence and robotics towards widespread robotic deployments that impact our everyday lives.
I completed my doctoral thesis under the supervision of Professor Brian Scassellati in the Department of Computer Science at Yale University in 2014. An important research interest of mine is the emulation of the developmental process by which infants develop self-awareness, as modeled in robots. My dissertation topic was the related topic of Robot Self-Modeling, by which robots learn about their bodies and senses through experience. I also worked on problems in human-robot interaction and contributed to the mechanical design and machining of our humanoid robot, Nico.
Previously I worked as a postdoctoral fellow in the Department of Mechanical Engineering at the University of British Columbia under the supervision of Elizabeth Croft. There, I researched human-robot collaborative manufacturing, and, in particular, the prediction of reach trajectories; which would allow for robots to predict human behavior in order to improve the speed and fluidity of physical collaboration.
My core research interests are artificial intelligence, human-robot interaction, and robot self-modeling. In particular, I am interested in themes in which we model human intelligence, leverage knowledge of human behavior, or take inspiration from human behavior. Additionally, I am interested in themes which I believe are likely to shape the direction of robotics and move robots into homes, workspaces, and public places, such as service robots. The dual goals of my research are to better understand human intelligence and to push the fields of artificial intelligence and robotics towards widespread robotic deployments that impact our everyday lives.