  We present SLOT-V, a novel supervised learning framework that learns observer
models (human preferences) from robot motion trajectories in a legibility
context. Legibility measures how easily a (human) observer can infer the
robot's goal from a robot motion trajectory. When generating such trajectories,
existing planners often rely on an observer model that estimates the quality of
trajectory candidates. These observer models are frequently hand-crafted or,
occasionally, learned from demonstrations. Here, we propose to learn them in a
supervised manner using the same data format that is frequently used during the
evaluation of aforementioned approaches. We then demonstrate the generality of
SLOT-V using a Franka Emika in a simulated manipulation environment. For this,
we show that it can learn to closely predict various hand-crafted observer
models, i.e., that SLOT-V's hypothesis space encompasses existing handcrafted
models. Next, we showcase SLOT-V's ability to generalize by showing that a
trained model continues to perform well in environments with unseen goal
configurations and/or goal counts. Finally, we benchmark SLOT-V's sample
efficiency (and performance) against an existing IRL approach and show that
SLOT-V learns better observer models with less data. Combined, these results
suggest that SLOT-V can learn viable observer models. Better observer models
imply more legible trajectories, which may - in turn - lead to better and more
transparent human-robot interaction.

Robotics