Stanford Seminar - Objects, Skills, and the Quest for Compositional Robot Autonomy
Explore the future of robot autonomy in this Stanford seminar featuring Yuke Zhu from UT Austin. Delve into the integration of deep learning advances with engineering principles to create scalable autonomous systems. Learn about state-action abstractions and their role in developing a compositional autonomy stack. Discover GIGA and Ditto for learning actionable object representations, and BUDS and MAPLE for scaffolding long-horizon tasks with sensorimotor skills. Gain insights into the challenges of generalization and robustness in robot learning algorithms, and explore potential solutions for widespread deployment. Engage with discussions on future research directions aimed at building scalable robot autonomy, including topics such as the James Webb Space Telescope, neural task programming, robotic grasping, and interactive digital training.