  To achieve seamless human-robot interactions, robots need to intimately
reason about complex interaction dynamics and future human behaviors within
their motion planning process. However, there is a disconnect between
state-of-the-art neural network-based human behavior models and robot motion
planners -- either the behavior models are limited in their consideration of
downstream planning or a simplified behavior model is used to ensure
tractability of the planning problem. In this work, we present a framework that
fuses together the interpretability and flexibility of trajectory optimization
(TO) with the predictive power of state-of-the-art human trajectory prediction
models. In particular, we leverage gradient information from data-driven
prediction models to explicitly reason about human-robot interaction dynamics
within a gradient-based TO problem. We demonstrate the efficacy of our approach
in a multi-agent scenario whereby a robot is required to safely and efficiently
navigate through a crowd of up to ten pedestrians. We compare against a variety
of planning methods, and show that by explicitly accounting for interaction
dynamics within the planner, our method offers safer and more efficient
behaviors, even yielding proactive and nuanced behaviors such as waiting for a
pedestrian to pass before moving.
