  This work aims to tackle the intent recognition problem in Human-Robot
Collaborative assembly scenarios. Precisely, we consider an interactive
assembly of a wooden stool where the robot fetches the pieces in the correct
order and the human builds the parts following the instruction manual. The
intent recognition is limited to the idle state estimation and it is needed to
ensure a better synchronization between the two agents. We carried out a
comparison between two distinct solutions involving wearable sensors and eye
tracking integrated into the perception pipeline of a flexible planning
architecture based on Hierarchical Task Networks. At runtime, the wearable
sensing module exploits the raw measurements from four 9-axis Inertial
Measurement Units positioned on the wrists and hands of the user as an input
for a Long Short-Term Memory Network. On the other hand, the eye tracking
relies on a Head Mounted Display and Unreal Engine.
  We tested the effectiveness of the two approaches with 10 participants, each
of whom explored both options in alternate order. We collected explicit metrics
about the attractiveness and efficiency of the two techniques through User
Experience Questionnaires as well as implicit criteria regarding the
classification time and the overall assembly time.
  The results of our work show that the two methods can reach comparable
performances both in terms of effectiveness and user preference. Future
development could aim at joining the two approaches two allow the recognition
of more complex activities and to anticipate the user actions.
