Reactive Semantic Planning in Unexplored Semantic Environments Using Deep Perceptual Feedback

• Published in: IEEE robotics and automation letters 2020-07, Vol.5 (3), p.4455-4462
• Main Authors: Vasilopoulos, Vasileios, Pavlakos, Georgios, Bowman, Sean L., Caporale, J. Diego, Daniilidis, Kostas, Pappas, George J., Koditschek, Daniel E.
• Format: Article
• Language: English
• Subjects: Algorithms; Architecture; Collision avoidance; motion and path planning; Moving targets; Object recognition; Planning; Reactive and sensor-based planning; Representations; Robustness (mathematics); semantic scene understanding; Semantics; Simultaneous localization and mapping; Target tracking
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2020.3001496

This letter 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.