# Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails

## TL;DR

This paper presents a virtual-to-real-world transfer learning approach for outdoor trail navigation in robots, using synthetic data to train deep learning models that perform well on real-world images, reducing the need for extensive real data collection.

## Contribution

It introduces a method to train trail classification models on synthetic data for outdoor navigation, enabling effective transfer to real-world environments without large real datasets.

## Key findings

- Models achieve over 95% accuracy on synthetic data
- Successful deployment in simulated robot control systems
- Demonstrated potential transferability to real-world trail data

## Abstract

Robots hold promise in many scenarios involving outdoor use, such as search-and-rescue, wildlife management, and collecting data to improve environment, climate, and weather forecasting. However, autonomous navigation of outdoor trails remains a challenging problem. Recent work has sought to address this issue using deep learning. Although this approach has achieved state-of-the-art results, the deep learning paradigm may be limited due to a reliance on large amounts of annotated training data. Collecting and curating training datasets may not be feasible or practical in many situations, especially as trail conditions may change due to seasonal weather variations, storms, and natural erosion. In this paper, we explore an approach to address this issue through virtual-to-real-world transfer learning using a variety of deep learning models trained to classify the direction of a trail in an image. Our approach utilizes synthetic data gathered from virtual environments for model training, bypassing the need to collect a large amount of real images of the outdoors. We validate our approach in three main ways. First, we demonstrate that our models achieve classification accuracies upwards of 95% on our synthetic data set. Next, we utilize our classification models in the control system of a simulated robot to demonstrate feasibility. Finally, we evaluate our models on real-world trail data and demonstrate the potential of virtual-to-real-world transfer learning.