Explore a comprehensive approach to robotic pick-and-place that combines high precision and generalization skills. Learn how simulation-based probabilistic models for grasping, planning, and localization can be effectively transferred to real robotic systems. Discover the capabilities of a dual-arm robot in exerting task-aware picks on new objects, utilizing visuo-tactile sensing for localization, and performing dexterous placings with sub-millimeter accuracy. Gain insights into achieving precise robotic generalization through learned probabilistic models, enabling robots to adapt their skills across multiple tasks. Delve into the research of Maria Bauza Villalonga, a PhD student at MIT, as she presents her work on advancing robotic capabilities in handling novel objects and placing configurations with high precision.