  Autonomous systems and humans are increasingly sharing the same space. Robots
work side by side or even hand in hand with humans to balance each other's
limitations. Such cooperative interactions are ever more sophisticated. Thus,
the ability to reason not just about a human's center of gravity position, but
also its granular motion is an important prerequisite for human-robot
interaction. Though, many algorithms ignore the multimodal nature of humans or
neglect uncertainty in their motion forecasts. We present Motron, a multimodal,
probabilistic, graph-structured model, that captures human's multimodality
using probabilistic methods while being able to output deterministic
maximum-likelihood motions and corresponding confidence values for each mode.
Our model aims to be tightly integrated with the robotic
planning-control-interaction loop; outputting physically feasible human motions
and being computationally efficient. We demonstrate the performance of our
model on several challenging real-world motion forecasting datasets,
outperforming a wide array of generative/variational methods while providing
state-of-the-art single-output motions if required. Both using significantly
less computational power than state-of-the art algorithms.
