  We focus on robot navigation in crowded environments. To navigate safely and
efficiently within crowds, robots need models for crowd motion prediction.
Building such models is hard due to the high dimensionality of multiagent
domains and the challenge of collecting or simulating interaction-rich
crowd-robot demonstrations. While there has been important progress on models
for offline pedestrian motion forecasting, transferring their performance on
real robots is nontrivial due to close interaction settings and novelty effects
on users. In this paper, we investigate the utility of a recent
state-of-the-art motion prediction model (S-GAN) for crowd navigation tasks. We
incorporate this model into a model predictive controller (MPC) and deploy it
on a self-balancing robot which we subject to a diverse range of crowd
behaviors in the lab. We demonstrate that while S-GAN motion prediction
accuracy transfers to the real world, its value is not reflected on navigation
performance, measured with respect to safety and efficiency; in fact, the MPC
performs indistinguishably even when using a simple constant-velocity
prediction model, suggesting that substantial model improvements might be
needed to yield significant gains for crowd navigation tasks. Footage from our
experiments can be found at https://youtu.be/mzFiXg8KsZ0.
