A Framework for Efficient Robotic Manipulation

• URL: http://arxiv.org/abs/2012.07975v1
• Date: Mon, 14 Dec 2020 22:18:39 GMT
• Title: A Framework for Efficient Robotic Manipulation
• Authors: Albert Zhan, Philip Zhao, Lerrel Pinto, Pieter Abbeel, Michael Laskin
• Abstract summary: We show that a single robotic arm can learn sparse-reward manipulation policies from pixels. We show that, given only 10 demonstrations, a single robotic arm can learn sparse-reward manipulation policies from pixels.
• Score: 79.10407063260473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data-efficient learning of manipulation policies from visual observations is an outstanding challenge for real-robot learning. While deep reinforcement learning (RL) algorithms have shown success learning policies from visual observations, they still require an impractical number of real-world data samples to learn effective policies. However, recent advances in unsupervised representation learning and data augmentation significantly improved the sample efficiency of training RL policies on common simulated benchmarks. Building on these advances, we present a Framework for Efficient Robotic Manipulation (FERM) that utilizes data augmentation and unsupervised learning to achieve extremely sample-efficient training of robotic manipulation policies with sparse rewards. We show that, given only 10 demonstrations, a single robotic arm can learn sparse-reward manipulation policies from pixels, such as reaching, picking, moving, pulling a large object, flipping a switch, and opening a drawer in just 15-50 minutes of real-world training time. We include videos, code, and additional information on the project website -- https://sites.google.com/view/efficient-robotic-manipulation.

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