ARTiS: Appearance-based Action Recognition in Task Space for Real-Time Human-Robot Collaboration
Abstract
To have a robot actively supporting a human during a collaborative task, it is crucial that robots are able to identify the current action in order to predict the next one. Common approaches make use of high-level knowledge, such as object affordances, semantics or understanding of actions in terms of pre- and post-conditions. These approaches often require hand-coded a priori knowledge, time- and resource-intensive or supervised learning techniques. We propose to reframe this problem as an appearance-based place recognition problem. In our framework, we regard sequences of visual images of human actions as a map in analogy to the visual place recognition problem. Observing the task for the second time, our approach is able to recognize pre-observed actions in a one-shot learning approach and is thereby able to recognize the current observation in the task space. We propose two new methods for creating and aligning action observations within a task map. We compare and verify our approaches with real data of humans assembling several types of IKEA flat packs.
Cite
@article{arxiv.1610.05432,
title = {ARTiS: Appearance-based Action Recognition in Task Space for Real-Time Human-Robot Collaboration},
author = {Markus Eich and Sareh Shirazi and Gordon Wyeth},
journal= {arXiv preprint arXiv:1610.05432},
year = {2017}
}