论文标题
技术报告：使用深度感知反馈的未开发语义环境中的反应性语义计划
Technical Report: Reactive Semantic Planning in Unexplored Semantic Environments Using Deep Perceptual Feedback
论文作者
论文摘要
本文提出了一种反应性计划系统，该系统丰富了具有紧密整合的语义表示环境的拓扑表示，这是通过纳入和利用深厚的感知学习和概率的语义上的语义推理来实现的。我们的体系结构将对象检测与语义大满贯结合在一起，在未探索的环境中提供了强大的反应性逻辑和几何规划。此外，通过结合人类网格估计算法，我们的系统能够对语义标记的人类动作和手势进行实时反应和反应。新的正式结果允许跟踪适当的非对抗性移动目标，同时保持相同的避免碰撞保证。我们建议通过数值研究提出的控制架构的经验实用性，包括与最先进的动态重建算法进行比较，以及在具有几何和语义范围的不同设置中的轮式和腿部平台上的物理实现。
This paper presents a reactive planning system that enriches the topological representation of an environment with a tightly integrated semantic representation, achieved by incorporating and exploiting advances in deep perceptual learning and probabilistic semantic reasoning. Our architecture combines object detection with semantic SLAM, affording robust, reactive logical as well as geometric planning in unexplored environments. Moreover, by incorporating a human mesh estimation algorithm, our system is capable of reacting and responding in real time to semantically labeled human motions and gestures. New formal results allow tracking of suitably non-adversarial moving targets, while maintaining the same collision avoidance guarantees. We suggest the empirical utility of the proposed control architecture with a numerical study including comparisons with a state-of-the-art dynamic replanning algorithm, and physical implementation on both a wheeled and legged platform in different settings with both geometric and semantic goals.
