Motron: マルチモーダル確率的な人間の動作予測
Motron: Multimodal Probabilistic Human Motion Forecasting
人間の動作をマルチモーダルかつ確率的に予測するグラフ構造モデルMotronを提案し、ロボットの計画制御ループに統合可能な高速・高精度予測を実現した。
著者: Tim Salzmann, Marco Pavone, Markus Ryll
分類: cs.CV, cs.HC, cs.LG, cs.RO
原文アブストラクト
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.
