In this paper, we develop an embodied AI system for human-in-the-loop navigation with a wheeled mobile robot. We propose a direct yet effective method of monitoring the robot's current plan to detect changes in the environment that impact the intended trajectory of the robot significantly and then query a human for feedback. We also develop a means to parse human feedback expressed in natural language into local navigation waypoints and integrate it into a global planning system, by leveraging a map of semantic features and an aligned obstacle map. Extensive testing in simulation and physical hardware experiments with a resource-constrained wheeled robot tasked to navigate in a real-world environment validate the efficacy and robustness of our method. This work can support applications like precision agriculture and construction, where persistent monitoring of the environment provides a human with information about the environment state.
@article{arxiv.2409.19459,
title = {Language-guided Robust Navigation for Mobile Robots in Dynamically-changing Environments},
author = {Cody Simons and Zhichao Liu and Brandon Marcus and Amit K. Roy-Chowdhury and Konstantinos Karydis},
journal= {arXiv preprint arXiv:2409.19459},
year = {2024}
}