Enhancing Robot Learning through Learned Human-Attention Feature Maps
Daniel Scheuchenstuhl Stefan Ulmer Felix Resch Luigi Berducci Radu Grosu
Robust and efficient learning remains a challenging problem in robotics, in
particular with complex visual inputs. Inspired by human attention mechanism,
with which we quickly process complex visual scenes and react to changes in the
environment, we think that embedding auxiliary information about focus point
into robot learning would enhance efficiency and robustness of the learning
process. In this paper, we propose a novel approach to model and emulate the
human attention with an approximate prediction model. We then leverage this
output and feed it as a structured auxiliary feature map into downstream
learning tasks. We validate this idea by learning a prediction model from
human-gaze recordings of manual driving in the real world. We test our approach
on two learning tasks - object detection and imitation learning. Our
experiments demonstrate that the inclusion of predicted human attention leads
to improved robustness of the trained models to out-of-distribution samples and
faster learning in low-data regime settings. Our work highlights the potential
of incorporating structured auxiliary information in representation learning
for robotics and opens up new avenues for research in this direction. All code
and data are available online.