Learning Autonomy: Off-Road Navigation Enhanced by Human Input

2025-02-26

AI Summary

Autonomous navigation in unstructured off-road environments (e.g., grass, mud, shrubbery, puddles) suffers from high data dependency and poor generalization. Method: This paper proposes an end-to-end local planning framework trained on minimal human demonstrations—only 5–10 minutes of monocular video—using a lightweight imitation learning architecture that jointly models monocular visual perception and motion prediction, enabling real-time path generation and dynamic obstacle avoidance without manual tuning. Contribution/Results: To our knowledge, this is the first work to demonstrate rapid generalization across diverse off-road terrains using such minimal demonstration data. It significantly reduces reliance on large-scale annotated datasets. Experiments show a 42% improvement in navigation success rate, two orders-of-magnitude higher data efficiency compared to conventional methods, strong robustness under environmental variability, and plug-and-play deployability on embedded platforms.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Off-road autonomous navigation challenges
• Learning human driving nuances
• Minimal human demonstration data

Innovation

• Learning-based local planner
• Captures human driving nuances
• Minimal human demonstration data