Learning End-to-End Action Interaction by Paired-Embedding Data Augmentation
This work addresses the issue of rigid robot-human interactions for robotics applications, presenting a novel task and method with incremental improvements in data augmentation.
The paper tackles the problem of stiff robot responses in action interaction by introducing the Interactive Action Translation (IAT) task to learn end-to-end interactions from unlabeled pairs, eliminating explicit action recognition, and achieves impressive results on two datasets.
In recognition-based action interaction, robots' responses to human actions are often pre-designed according to recognized categories and thus stiff. In this paper, we specify a new Interactive Action Translation (IAT) task which aims to learn end-to-end action interaction from unlabeled interactive pairs, removing explicit action recognition. To enable learning on small-scale data, we propose a Paired-Embedding (PE) method for effective and reliable data augmentation. Specifically, our method first utilizes paired relationships to cluster individual actions in an embedding space. Then two actions originally paired can be replaced with other actions in their respective neighborhood, assembling into new pairs. An Act2Act network based on conditional GAN follows to learn from augmented data. Besides, IAT-test and IAT-train scores are specifically proposed for evaluating methods on our task. Experimental results on two datasets show impressive effects and broad application prospects of our method.