# Learning to Gather Information via Imitation

arXiv: https://arxiv.org/abs/1611.04180 · HTML: https://mlsanity.com/paper/1611.04180/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2016.

## Built on by (most cited first, 4 of 4)

- [Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning](https://mlsanity.com/paper/1805.11240/) (2018)
- [IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learning](https://mlsanity.com/paper/2405.01472/) (2024)
- [Graph Neural Networks for Multi-Robot Active Information Acquisition](https://mlsanity.com/paper/2209.12091/) (2022)
- [Map Space Belief Prediction for Manipulation-Enhanced Mapping](https://mlsanity.com/paper/2502.20606/) (2025)

## Abstract (arXiv)

The budgeted information gathering problem - where a robot with a fixed fuel budget is required to maximize the amount of information gathered from the world - appears in practice across a wide range of applications in autonomous exploration and inspection with mobile robots. Although there is an extensive amount of prior work investigating effective approximations of the problem, these methods do not address the fact that their performance is heavily dependent on distribution of objects in the world. In this paper, we attempt to address this issue by proposing a novel data-driven imitation learning framework. We present an efficient algorithm, EXPLORE, that trains a policy on the target distribution to imitate a clairvoyant oracle - an oracle that has full information about the world and computes non-myopic solutions to maximize information gathered. We validate the approach on a spectrum of results on a number of 2D and 3D exploration problems that demonstrates the ability of EXPLORE to adapt to different object distributions. Additionally, our analysis provides theoretical insight into the behavior of EXPLORE. Our approach paves the way forward for efficiently applying data-driven methods to the domain of information gathering.