  Robots need models of human behavior for both inferring human goals and
preferences, and predicting what people will do. A common model is the
Boltzmann noisily-rational decision model, which assumes people approximately
optimize a reward function and choose trajectories in proportion to their
exponentiated reward. While this model has been successful in a variety of
robotics domains, its roots lie in econometrics, and in modeling decisions
among different discrete options, each with its own utility or reward. In
contrast, human trajectories lie in a continuous space, with continuous-valued
features that influence the reward function. We propose that it is time to
rethink the Boltzmann model, and design it from the ground up to operate over
such trajectory spaces. We introduce a model that explicitly accounts for
distances between trajectories, rather than only their rewards. Rather than
each trajectory affecting the decision independently, similar trajectories now
affect the decision together. We start by showing that our model better
explains human behavior in a user study. We then analyze the implications this
has for robot inference, first in toy environments where we have ground truth
and find more accurate inference, and finally for a 7DOF robot arm learning
from user demonstrations.
