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• W2913937523 abstract "We present a visual imitation learning method that enables robots to imitate demonstrated skills by learning a perceptual reward function based on object-centric feature representations. Our method uses the background configuration of the scene to compute object masks for the objects present. The robotic agent then trains a detector for the relevant objects in the scene via a process we call data dreaming, generating a synthetic dataset of images of various object occlusion configurations using only a small amount of background-subtracted ground truth images. We use the output of the object detector to learn an object-centric visual feature representation. We show that the resulting factorized feature representation comprised of per-object appearance features and cross-object relative locations enables efficient real world reinforcement learning that can teach a robot a policy based on a single demonstration after few minutes of training." @default.
• W2913937523 created "2019-02-21" @default.
• W2913937523 creator A5008661738 @default.
• W2913937523 creator A5037849180 @default.
• W2913937523 date "2018-11-01" @default.
• W2913937523 modified "2023-10-18" @default.
• W2913937523 title "Data Dreaming for Object Detection: Learning Object-Centric State Representations for Visual Imitation" @default.
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• W2913937523 doi "https://doi.org/10.1109/humanoids.2018.8625007" @default.
• W2913937523 hasPublicationYear "2018" @default.
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