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Spatio-semantic Task Recognition: Unsupervised Learning of Task-discriminative Features for Segmentation and Imitation

J.hyeon Park, Jigang Kim, and H. Jin Kim*
International Journal of Control, Automation, and Systems, vol. 19, no. 10, pp.3409-3418, 2021

Abstract : Discovering task subsequences from a continuous video stream facilitates a robot imitation of sequential tasks. In this research, we develop unsupervised learning of the task subsequences which does not require a human teacher to give the supervised label of the subsequence. Task-discriminative feature, in the form of sparsely activated cells called task capsules, is proposed for self-training to preserve spatio-semantic information of a visual input. The task capsules are sparsely and exclusively activated with respect to the spatio-semantic context of the task subsequence: a type and location of the object. Therefore, the generalized purpose in multiple videos is unsupervisedly discovered according to the spatio-semantic context, and the demonstration is segmented into the task subsequences in an object-centric way. In comparison with the existing studies on unsupervised task segmentation, our work has the following distinct contribution: 1) the task provided as a video stream can be segmented without any pre-defined knowledge, 2) the trained features preserve spatio-semantic information so that the segmentation is object-centric. Our experiment shows that the recognition of the task subsequence can be applied to robot imitation for a sequential pick-and-place task by providing the semantic and location information of the object to be manipulated.

Keyword : Capsule network, robot imitation, task segmentation.

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