Learning Autonomy: Off-Road Navigation Enhanced by Human Input

TL;DR

This paper introduces a learning-based local planner for off-road autonomous navigation that captures human driving nuances from minimal real-world demonstrations, enabling quick adaptation to diverse terrains and obstacles.

Contribution

It presents a novel monocular camera-based learning approach that efficiently learns human driving behaviors for off-road navigation with minimal data and no manual fine-tuning.

Findings

Successfully navigates various off-road terrains and obstacles.

Requires only 5-10 minutes of demonstration data.

Demonstrates rapid learning and adaptability in real-world scenarios.

Abstract

In the area of autonomous driving, navigating off-road terrains presents a unique set of challenges, from unpredictable surfaces like grass and dirt to unexpected obstacles such as bushes and puddles. In this work, we present a novel learning-based local planner that addresses these challenges by directly capturing human driving nuances from real-world demonstrations using only a monocular camera. The key features of our planner are its ability to navigate in challenging off-road environments with various terrain types and its fast learning capabilities. By utilizing minimal human demonstration data (5-10 mins), it quickly learns to navigate in a wide array of off-road conditions. The local planner significantly reduces the real world data required to learn human driving preferences. This allows the planner to apply learned behaviors to real-world scenarios without the need for manual fine-tuning, demonstrating quick adjustment and adaptability in off-road autonomous driving technology.