Technical Report: Reactive Semantic Planning in Unexplored Semantic Environments Using Deep Perceptual Feedback

TL;DR

This paper introduces a reactive semantic planning system that integrates deep perceptual learning with semantic SLAM, enabling real-time, robust navigation and human interaction in unexplored environments.

Contribution

It combines object detection, semantic SLAM, and human motion estimation into a unified reactive planning architecture with formal collision guarantees.

Findings

Demonstrates robust navigation in unknown environments.

Shows real-time response to human gestures and motions.

Validates effectiveness through physical platform experiments.

Abstract

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.