Talk: Project Dexter: Machine learning and automatic decision-making for robotic manipulation

Speakers: Andrey Kolobov, Principal Researcher, Microsoft Research Redmond Ching-An Cheng, Senior Researcher, Microsoft Research Redmond Robot technology has long held the promise of disrupting many important industries that involve dexterous object manipulation in weakly structured environments, including healthcare, agriculture, and infrastructure maintenance. The increasing versatility of robotic manipulation hardware seemingly puts this disruption within reach. However, hardware versatility comes at a cost. Its complexity defies traditional control-based approaches, and recent success stories, such as a dexterous robotic hand assembling a Rubik’s cube, highlight the reality: it can take world-class roboticists dozens of person-years to train an advanced robotic manipulator to solve a single problem with modern machine learning–based sequential decision-making techniques. The goal of Microsoft Research’s Project Dexter is to enable training robotic manipulation policies for real-world tasks at a practical