# Fast Incremental Learning for Off-Road Robot Navigation

arXiv: https://arxiv.org/abs/1606.08057 · HTML: https://mlsanity.com/paper/1606.08057/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2016.

## Built on by (most cited first, 1 of 1)

- [Consensus Based Multi-Layer Perceptrons for Edge Computing](https://mlsanity.com/paper/2102.05021/) (2021)

## Abstract (arXiv)

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.