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Scalable Deep Reinforcement Learning for Robotic Manipulation

jueves, 28 de junio de 2018

Posted Alex Irpan, Software Engineer, Google Brain Team and Peter Pastor, Senior Roboticist, Xdeep learningreinforcement learningprevious work

Examples of singulation.large-scale distributed optimizationQ-learningarXiv

Some of the training objects used.

that sharing experience across robots can accelerate learning

Seven robots collecting grasp data.

The objects used at evaluation time. To make the task challenging, we aimed for a large variety of object sizes, textures, and shapes.

When presented with a set of interlocking blocks that cannot be picked up together, the policy separates one of the blocks from the rest before picking it up.

When presented with a difficult-to-grasp object, the policy figures out it should reposition the gripper and regrasp it until it has a firm hold.

When grasping in clutter, the policy probes different objects until the fingers hold one of them firmly, before lifting.

When we perturbed the robot by intentionally swatting the object out of the gripper -- something it had not seen during training -- it automatically repositioned the gripper for another attempt.

Examples of the learned behaviors. In the left GIF, the policy corrects for the moved ball. In the right GIF, the policy tries several grasps until it succeeds at picking up the tricky object.domain adaptationrecent work on learning how to self-calibrateAcknowledgementsThis research was conducted by Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, and Sergey Levine. We’d also like to give special thanks to Iñaki Gonzalo and John-Michael Burke for overseeing the robot operations, Chelsea Finn, Timothy Lillicrap, and Arun Nair for valuable discussions, and other people at Google and X who’ve contributed their expertise and time towards this research. A preprint is available on arXiv.