Justin Hart

Justin Hart is a Ph.D. Candidate in the Department of Computer Science at Yale University, where he is advised by Professor Brian Scassellati. His research focuses on robotic self-modeling, in which robots learn models of their bodies and senses through data sampled during operation. He also has performed signifcant work in human-robot interaction, including studies on creating trust by manipulating social presence, attributions of agency, and the creation of lifelike motion. His work has recently been featured in the Society of Manufacturing Engineers Innovation Watch List, and has appeared in media outlets such as New Scientist, BBC News, GE Focus Foward Films, and Google Solve for X.

 

Justin is interested in constructing robots which autonomously learn about their sensorimotor capabilities, breaking from the standard practice in which such models, if present at all, are constructed during the design of the robot, by engineers, or meticulously calibrated offline. This process is called robot self-modeling. If robots are able to understand themselves in this way, it will open the possibility of highly robust machines that are able to adapt flexibly to different use cases or to damage, in software, and very precise machines that continuously self-calibrate. The models that these robots construct are inspired by the process by which children learn about their sensory and physical capabilities and how they are able to interact with the environment, which represent the earliest forms of self-awareness to develop during infancy.

While Justin’s thesis research focuses on robots autonomously learning about their bodies and senses, he also works in human-robot interaction, including projects on social presence, attributions of agency, and creating lifelike motion.

 

 

Camping, backpacking, spending time outdoors, rock climbing, cars, and cooking.