Motron: Multimodal Probabilistic Human Motion Forecasting
- URL: http://arxiv.org/abs/2203.04132v1
- Date: Tue, 8 Mar 2022 14:58:41 GMT
- Title: Motron: Multimodal Probabilistic Human Motion Forecasting
- Authors: Tim Salzmann, Marco Pavone, Markus Ryll
- Abstract summary: Motron is a graph-structured model that captures human's multimodality.
It outputs deterministic motions and corresponding confidence values for each mode.
We demonstrate the performance of our model on several challenging real-world motion forecasting datasets.
- Score: 30.154996245556532
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 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 methods
while providing state-of-the-art deterministic motions if required. Both using
significantly less computational power than state-of-the art algorithms.
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