Automated acquisition of structured, semantic models of manipulation
  activities from human VR demonstration
  In this paper we present a system capable of collecting and annotating, human
performed, robot understandable, everyday activities from virtual environments.
The human movements are mapped in the simulated world using off-the-shelf
virtual reality devices with full body, and eye tracking capabilities. All the
interactions in the virtual world are physically simulated, thus movements and
their effects are closely relatable to the real world. During the activity
execution, a subsymbolic data logger is recording the environment and the human
gaze on a per-frame basis, enabling offline scene reproduction and replays.
Coupled with the physics engine, online monitors (symbolic data loggers) are
parsing (using various grammars) and recording events, actions, and their
effects in the simulated world.
