In this paper we present the game of cornhole as a compelling, accessible, and adaptable AI robotics task. Cornhole is a fun and social game with simple rules, but involves strategy and physical training for humans to play competitively; thus, developing a robot that can play at the level of even the average human player presents a multitude of opportunities for curricular integration at a variety of levels. We characterize the AI tasks involved with the game, and present results and resources gained from preliminary offerings.

Human Computer Interaction (HCI) is central for many applications, including hazardous environment inspection and telemedicine. Whereas traditional methods ofHCI for teleoperating electromechanical systems include joysticks, levers, or buttons, our research focuses on using electromyography (EMG) signals to improve intuition and response time. An important challenge is to accurately and efficiently extract and map EMG signals to known position for real-time control. In this preliminary work, we compare the accuracy and real-time performance of several machine-learning techniques for recognizing specific arm positions. We present results from offline analysis, as well as end-to-end operation using a robotic arm.