Explore diverse data collection and efficient algorithms for robot learning in this MIT Embodied Intelligence Seminar featuring Lerrel Pinto from NYU and UC Berkeley. Delve into the challenges of applying machine learning to robotics, focusing on large-scale data collection and efficient reinforcement learning for deformable object manipulation. Discover self-supervised grasping techniques, robot learning in homes, and the importance of forward models in robotic systems. Gain insights into conditional policy learning, contrastive representations, and one-step model predictive control. Examine quantitative evaluations and real-world robot experiments that demonstrate the practical applications of these cutting-edge approaches in robotics.