EFFICIENT ADAPTION OF ROBOT CONTROL POLICY FOR NEW TASK USING META-LEARNING BASED ON META-IMITATION LEARNING AND META-REINFORCEMENT LEARNING

摘要

Techniques are disclosed that enable training a meta-learning model, for use in causing a robot to perform a task, using imitation learning as well as reinforcement learning. Some implementations relate to training the meta-learning model using imitation learning based on one or more human guided demonstrations of the task. Additional or alternative implementations relate to training the meta-learning model using reinforcement learning based on trials of the robot attempting to perform the task. Further implementations relate to using the trained meta-learning model to few shot (or one shot) learn a new task based on a human guided demonstration of the new task.