Graph-Structured Visual Imitation

Maximilian Sieb, Zhou Xian, Audrey Huang, Oliver Kroemer and Katerina Fragkiadaki
Conference Paper, Proceedings of (CoRL) Conference on Robot Learning, October, 2019

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We cast visual imitation as a visual correspondence problem. Our robotic agent is rewarded when its actions result in better matching of relative spatial configurations for corresponding visual entities detected in its workspace and the teacher’s demonstration. We build upon recent advances in Computer Vision, such as human finger keypoint detectors, object detectors trained on-the- fly with synthetic augmentations, and point detectors supervised by viewpoint changes and learn multiple visual entity detectors for each demonstration with- out human annotations or robot interactions. We empirically show that the pro- posed factorized visual representations of entities and their spatial arrangements drive successful imitation of a variety of manipulation skills within minutes, us- ing a single demonstration and without any environment instrumentation. It is robust to background clutter and can effectively generalize across environment variations between demonstrator and imitator, greatly outperforming unstructured non-factorized full-frame CNN encodings of previous works.

Joint first authorship

author = {Maximilian Sieb and Zhou Xian and Audrey Huang and Oliver Kroemer and Katerina Fragkiadaki},
title = {Graph-Structured Visual Imitation},
booktitle = {Proceedings of (CoRL) Conference on Robot Learning},
year = {2019},
month = {October},
} 2019-11-08T08:05:39-05:00