Google Inc.

X Development LLC

Daniel Kappler

Seyed Mohammad Khansari Zadeh

Sergey Levine

Yunfei Bai

Eric Jang

Chelsea Finn

Mrinal Kalakrishnan

Daniel Alex Lam

Matthew Bennice

Alexander Irpan

Brent Austin

Nicolas Sievers

Jeffrey Bingham

Paul Wohlhart

Alexander Herzog

Jianlan Luo

Allan Zhou

Implementations described herein relate to training and refining robotic control policies using imitation learning techniques. A robotic control policy can be initially trained based on human demonstrations of various robotic tasks. Further, the robotic control policy can be refined based on human interventions while a robot is performing a robotic task. In some implementations, the robotic control policy may determine whether the robot will fail in performance of the robotic task, and prompt a human to intervene in performance of the robotic task. In additional or alternative implementations, a representation of the sequence of actions can be visually rendered for presentation to the human can proactively intervene in performance of the robotic task.

System(s) and method(s) of using imitation learning in training and refining robotic control policies

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.

Efficient adaption of robot control policy for new task using meta-learning based on meta-imitation learning and meta-reinforcement learning

Generating and utilizing action image(s) that represent a candidate pose (e.g., a candidate end effector pose), in determining whether to utilize the candidate pose in performance of a robotic task. The action image(s) and corresponding current image(s) can be processed, using a trained critic network, to generate a value that indicates a probability of success of the robotic task if component(s) of the robot are traversed to the particular pose. When the value satisfies one or more conditions (e.g., satisfies a threshold), the robot can be controlled to cause the component(s) to traverse to the particular pose in performing the robotic task.

Robotic control using action image(s) and critic network

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San Francisco, CA, United States of America

Daniel Kappler