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Peter Stone: Robot Skill Learning: From the Real World to Simulation and Back | CMU RI Seminar
Abstract: “For autonomous robots to operate in the open, dynamically changing world, they will need to be able to learn a robust set of interacting skills. This talk begins by introducing “Overlapping Layered Learning” as a novel hierarchical machine learning paradigm for learning such interacting skills in simulation. While learning in simulation is appealing because it avoids the prohibitive sample cost of learning in the real world, unfortunately policies learned in simulation often fail when applied on physical robots. This talk then introduces “Grounded Simulation Learning” to address this problem by algorithmically altering the simulator to better match the real world, and connects this new algorithm to a theoretical analysis of off-policy evaluation in reinforcement learning. Overlapping Layered Learning was the key deciding factor in UT Austin Villa’s RoboCup robot soccer 3D simulation league championship, and Grounded Simulation Learning has led to the fastest known stable walk on a widely used humanoid robot.”
