Interactive Imitation Learning: Planning Alongside Humans
Explore the challenges and advancements in interactive imitation learning for robots working alongside humans in this Stanford seminar. Dive into feedback-driven covariate shift and predicting human intent, examining unified distribution matching frameworks and graph neural network approaches. Learn how these methods contribute to self-driving technology deployed at scale. Discover solutions for adapting to individual human preferences, improving online learning, and understanding natural human interactions. Gain insights into the complexities of programming rules, Markov decision processes, and the limitations of infinite data. Analyze quantitative plots, non-realizable expert simulations, and driving simulators to understand the practical applications of these concepts. Conclude with an exploration of grammar modes, merging scenarios, and transformer networks in the context of interactive imitation learning.