Interactive Imitation Learning: Planning Alongside Humans

This course focuses on the challenges of designing robots to work alongside humans in real-world scenarios. The learning outcomes include understanding how robots can learn from natural human interactions and predict human intent. The course teaches skills such as tackling feedback-driven covariate shift and planning with learned forecasts using a graph neural network approach. The teaching method involves a seminar-style talk diving into two core challenges in interactive imitation learning. The intended audience for this course includes researchers, practitioners, and students interested in machine learning, robotics, and artificial intelligence.