  Robots that share an environment with humans may communicate their intent
using a variety of different channels. Movement is one of these channels and,
particularly in manipulation tasks, intent communication via movement is called
legibility. It alters a robot's trajectory to make it intent expressive. Here
we propose a novel evaluation method that improves the data efficiency of
collected experimental data when benchmarking approaches generating such
legible behavior. The primary novelty of the proposed method is that it uses
trajectories that were generated independently of the framework being tested.
This makes evaluation easier, enables N-way comparisons between approaches, and
allows easier comparison across papers. We demonstrate the efficiency of the
new evaluation method by comparing 10 legibility frameworks in 2 scenarios. The
paper, thus, provides readers with (1) a novel approach to investigate and/or
benchmark legibility, (2) an overview of existing frameworks, (3) an evaluation
of 10 legibility frameworks (from 6 papers), and (4) evidence that viewing
angle and trajectory progression matter when users evaluate the legibility of a
motion.
