New Graduate Course, Spring 2020

Socially Cognizant Robotics

Students of the “Socially Cognizant Robotics” course will be exposed to basic principles and state-of-the-art developments of robotics so as to learn the expected trajectory of this technology, which will impact individuals and society. The course will be designed for both STEM students as well as computationally-oriented cognitive and social science students. This course is intended to provide students from interdisciplinary departments a necessary subset of methods and technical skills in robotics so as to allow them to study the social effects of robotics technology on individuals and society. This unique interdisciplinary course has seven underlying disciplines spanning STEM fields to social and behavioral sciences. It includes traditionally technical disciplines, such as robot embodiment and control, to areas which support human interaction, such as visual learning and language processing, to cognitive modeling, which enables more high level human-robot cooperation, and finally to areas, such as behavioral research and public policy. The course will utilize open-source software libraries in computer vision (OpenCV), robotics (ROS, CoppeliaSim), and deep learning (Pytorch) to make system development accessible to computationally oriented cognitive and social science students. Recent innovations at the intersection of deep reinforcement learning and human behavior modeling will be explored in the context of optimizing collaborative robot action.  

Specific learning goals for the students are as follows:

  • Develop an understanding of the nascent field of Socially Cognizant Robotics;
  • Learn to use robotic simulators for interdisciplinary research in robotics, cognitive science, and social sciences;
  • Learn an overview of robot control and embodiment principles in the context of human-robot collaboration that emphasizes pro-social performance metrics; 
  • Implement algorithms for visual learning, motion planning and language processing in human-robot interaction in the context of real-world tasks and scenes;
  • Learn cognitive modeling of human behavior in order to design better robotic systems that are tuned to human desires and that can be used to learn human intent;
  • Learn to approach robotics from a social science perspective to inform public policy.