Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration

• URL: http://arxiv.org/abs/2105.06411v2
• Date: Thu, 10 Jun 2021 17:58:27 GMT
• Title: Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration
• Authors: Edward Johns
• Abstract summary: We introduce a simple new method for visual imitation learning, which allows a novel robot manipulation task to be learned from a single human demonstration. Our method models imitation learning as a state estimation problem, with the state defined as the end-effector's pose. At test time, the end-effector moves to the estimated state through a linear path, at which point the original demonstration's end-effector velocities are simply replayed.
• Score: 8.57914821832517
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a simple new method for visual imitation learning, which allows a novel robot manipulation task to be learned from a single human demonstration, without requiring any prior knowledge of the object being interacted with. Our method models imitation learning as a state estimation problem, with the state defined as the end-effector's pose at the point where object interaction begins, as observed from the demonstration. By then modelling a manipulation task as a coarse, approach trajectory followed by a fine, interaction trajectory, this state estimator can be trained in a self-supervised manner, by automatically moving the end-effector's camera around the object. At test time, the end-effector moves to the estimated state through a linear path, at which point the original demonstration's end-effector velocities are simply replayed. This enables convenient acquisition of a complex interaction trajectory, without actually needing to explicitly learn a policy. Real-world experiments on 8 everyday tasks show that our method can learn a diverse range of skills from a single human demonstration, whilst also yielding a stable and interpretable controller.

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