ViNG: Learning Open-World Navigation with Visual Goals

TL;DR

ViNG is a learning-based navigation system enabling robots to reach visual goals in real-world environments by combining learned policies with topological graphs, outperforming prior methods and demonstrating versatility in applications like delivery and inspection.

Contribution

This work introduces ViNG, a novel method that integrates learned navigation policies with topological graphs for open-world visual goal navigation, addressing challenges of goal flexibility and environmental variability.

Findings

ViNG outperforms previous goal-conditioned reinforcement learning methods.

The system effectively generalizes to unseen environments.

ViNG successfully operates in real-world scenarios like delivery and inspection.

Abstract

We propose a learning-based navigation system for reaching visually indicated goals and demonstrate this system on a real mobile robot platform. Learning provides an appealing alternative to conventional methods for robotic navigation: instead of reasoning about environments in terms of geometry and maps, learning can enable a robot to learn about navigational affordances, understand what types of obstacles are traversable (e.g., tall grass) or not (e.g., walls), and generalize over patterns in the environment. However, unlike conventional planning algorithms, it is harder to change the goal for a learned policy during deployment. We propose a method for learning to navigate towards a goal image of the desired destination. By combining a learned policy with a topological graph constructed out of previously observed data, our system can determine how to reach this visually indicated goal even in the presence of variable appearance and lighting. Three key insights, waypoint proposal, graph pruning and negative mining, enable our method to learn to navigate in real-world environments using only offline data, a setting where prior methods struggle. We instantiate our method on a real outdoor ground robot and show that our system, which we call ViNG, outperforms previously-proposed methods for goal-conditioned reinforcement learning, including other methods that incorporate reinforcement learning and search. We also study how \sysName generalizes to unseen environments and evaluate its ability to adapt to such an environment with growing experience. Finally, we demonstrate ViNG on a number of real-world applications, such as last-mile delivery and warehouse inspection. We encourage the reader to visit the project website for videos of our experiments and demonstrations sites.google.com/view/ving-robot.