  Many underwater tasks, such as cable-and-wreckage inspection,
search-and-rescue, benefit from robust human-robot interaction (HRI)
capabilities. With the recent advancements in vision-based underwater HRI
methods, autonomous underwater vehicles (AUVs) can communicate with their human
partners even during a mission. However, these interactions usually require
active participation especially from humans (e.g., one must keep looking at the
robot during an interaction). Therefore, an AUV must know when to start
interacting with a human partner, i.e., if the human is paying attention to the
AUV or not. In this paper, we present a diver attention estimation framework
for AUVs to autonomously detect the attentiveness of a diver and then navigate
and reorient itself, if required, with respect to the diver to initiate an
interaction. The core element of the framework is a deep neural network (called
DATT-Net) which exploits the geometric relation among 10 facial keypoints of
the divers to determine their head orientation. Our on-the-bench experimental
evaluations (using unseen data) demonstrate that the proposed DATT-Net
architecture can determine the attentiveness of human divers with promising
accuracy. Our real-world experiments also confirm the efficacy of DATT-Net
which enables real-time inference and allows the AUV to position itself for an
AUV-diver interaction.
