# Fast Incremental Learning for Off-Road Robot Navigation

Source: [Fast Incremental Learning for Off-Road Robot Navigation](<https://www.sota2.com/research/paper/68fa629e1e209131ce9fa458>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:23:26.300Z

Authors: Artem Provodin, Liila Torabi, Beat Flepp, Yann LeCun, Michael Sergio, L. D. Jackel, Urs Muller, Jure Zbontar

Published: 2016-06-26T17:31:02.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1606.08057>)

[PDF](<https://arxiv.org/pdf/1606.08057.pdf>)