ARTiS: Appearance-based Action Recognition in Task Space for Real-Time Human-Robot Collaboration
This addresses the need for efficient, low-resource action recognition in human-robot collaboration, though it is incremental as it builds on place recognition concepts.
The paper tackles the problem of enabling robots to recognize human actions in real-time for collaboration by reframing it as an appearance-based place recognition problem, achieving one-shot learning with real data from IKEA assembly tasks.
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.