Neural MP: 汎用ニューラル動作計画器
Neural MP: A Generalist Neural Motion Planner
シミュレーションで大量の複雑シーンと専門家データを生成し、反応的な汎用方策へ蒸留して軽量最適化と組み合わせることで、実世界の動作計画を高速かつ高成功率で実現した。
著者: Murtaza Dalal, Jiahui Yang, Russell Mendonca, Youssef Khaky, Ruslan Salakhutdinov, Deepak Pathak
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
The current paradigm for motion planning generates solutions from scratch for every new problem, which consumes significant amounts of time and computational resources. For complex, cluttered scenes, motion planning approaches can often take minutes to produce a solution, while humans are able to accurately and safely reach any goal in seconds by leveraging their prior experience. We seek to do the same by applying data-driven learning at scale to the problem of motion planning. Our approach builds a large number of complex scenes in simulation, collects expert data from a motion planner, then distills it into a reactive generalist policy. We then combine this with lightweight optimization to obtain a safe path for real world deployment. We perform a thorough evaluation of our method on 64 motion planning tasks across four diverse environments with randomized poses, scenes and obstacles, in the real world, demonstrating an improvement of 23%, 17% and 79% motion planning success rate over state of the art sampling, optimization and learning based planning methods. Video results available at mihdalal.github.io/neuralmotionplanner
