  Prediction of human motions is key for safe navigation of autonomous robots
among humans. In cluttered environments, several motion hypotheses may exist
for a pedestrian, due to its interactions with the environment and other
pedestrians.
  Previous works for estimating multiple motion hypotheses require a large
number of samples which limits their applicability in real-time motion
planning. In this paper, we present a variational learning approach for
interaction-aware and multi-modal trajectory prediction based on deep
generative neural networks.
  Our approach can achieve faster convergence and requires significantly fewer
samples comparing to state-of-the-art methods. Experimental results on real and
simulation data show that our model can effectively learn to infer different
trajectories. We compare our method with three baseline approaches and present
performance results demonstrating that our generative model can achieve higher
accuracy for trajectory prediction by producing diverse trajectories.
