For autonomous systems, having the ability to predict the evolution of their surroundings is essential for safe, reliable, and efficient operation. Prediction is especially important when the autonomous system must interact and “negotiate” with humans, whether it be in settings that are cooperative, adversarial, or anywhere in between. This line of research involves quantifying the relative likelihoods of multiple, possibly highly distinct futures for interactive scenarios, planning strategies such that the autonomous agent is cognizant of how the human may respond, developing models that are offer transparency into the autonomous agent’s decision making process, and designing safe human-in-the-loop testing methodologies to validate our models and planning algorithms.
Keywords: Human-Robot Interaction, Deep Learning, Motion Planning
Students: Edward Schmerling, Karen Leung, Boris Ivanovic, Sandeep Chinchali