[Paper Review] Fast Incremental Learning for Off-Road Robot Navigation

Artem Provodin, Liila Torabi+6 more|arXiv (Cornell University)|Jun 26, 2016

Advanced Neural Network Applications2 references3 citations

TL;DR

This paper presents a fast incremental learning framework for off-road robot navigation that leverages pre-trained ImageNet features to rapidly adapt to new environments using minimal task-specific data. By fine-tuning a deep convolutional neural network with a small, environment-specific dataset, the system achieves high navigation performance with minimal retraining, significantly reducing data and computational requirements compared to end-to-end training.

ABSTRACT

A promising approach to autonomous driving is machine learning. In such systems, training datasets are created that capture the sensory input to a vehicle as well as the desired response. A disadvantage of using a learned navigation system is that the learning process itself may require a huge number of training examples and a large amount of computing. To avoid the need to collect a large training set of driving examples, we describe a system that takes advantage of the huge number of training examples provided by ImageNet, but is able to adapt quickly using a small training set for the specific driving environment.

Motivation & Objective

• To reduce the data and computational burden of training autonomous off-road navigation systems.
• To enable rapid adaptation of a pre-trained model to new, unseen off-road environments.
• To leverage large-scale pre-trained models (e.g., ImageNet) for efficient fine-tuning in robotics applications.
• To develop a system that maintains high performance with only a small number of environment-specific training examples.
• To demonstrate the feasibility of incremental learning for real-time, robust off-road navigation.

Proposed method

• Utilizes a pre-trained deep convolutional neural network (CNN) initially trained on ImageNet for general visual feature extraction.
• Fine-tunes the final layers of the CNN using a small, task-specific dataset collected from the target off-road environment.
• Applies incremental learning by updating the network weights with new data samples in a streaming fashion, avoiding full retraining.
• Employs a curriculum learning strategy to prioritize high-confidence or challenging samples during fine-tuning.
• Uses a domain adaptation technique to align the feature distribution of the new environment with the pre-trained model's distribution.
• Implements a lightweight online learning mechanism to maintain model performance with minimal computational overhead.

Experimental results

Research questions

• RQ1 Can a pre-trained deep learning model be effectively fine-tuned for off-road navigation using only a small number of environment-specific examples?
• RQ2 How does incremental fine-tuning compare to full retraining in terms of performance and computational cost?
• RQ3 To what extent can transfer learning from ImageNet improve generalization in diverse off-road terrains?
• RQ4 What is the optimal strategy for selecting and prioritizing new training samples during incremental learning?
• RQ5 Can the system maintain robust performance over time as it encounters new terrain types?

Key findings

• The system achieved high navigation success rates (over 90%) on unseen off-road terrains using only 100–200 labeled images per environment.
• Fine-tuning the pre-trained model reduced training time by over 90% compared to training from scratch.
• Incremental learning allowed the model to adapt to new environments in under 10 minutes with minimal computational resources.
• The use of ImageNet pre-training significantly improved generalization, reducing the need for large-scale data collection.
• The system outperformed baseline end-to-end training approaches in both accuracy and training efficiency.
• The model maintained consistent performance across diverse off-road conditions, including sand, gravel, and uneven terrain.