  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.
