# Curiosity Based Exploration for Learning Terrain Models

arXiv: https://arxiv.org/abs/1310.6767 · HTML: https://mlsanity.com/paper/1310.6767/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2013.

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

- [DEUX: Active Exploration for Learning Unsupervised Depth Perception](https://mlsanity.com/paper/2310.06164/) (2023)

## Abstract (arXiv)

We present a robotic exploration technique in which the goal is to learn to a visual model and be able to distinguish between different terrains and other visual components in an unknown environment. We use ROST, a realtime online spatiotemporal topic modeling framework to model these terrains using the observations made by the robot, and then use an information theoretic path planning technique to define the exploration path. We conduct experiments with aerial view and underwater datasets with millions of observations and varying path lengths, and find that paths that are biased towards locations with high topic perplexity produce better terrain models with high discriminative power, especially with paths of length close to the diameter of the world.