# An Upper Confidence Bound for Simultaneous Exploration and Exploitation in Heterogeneous Multi-Robot Systems

arXiv: https://arxiv.org/abs/2105.06118 · HTML: https://mlsanity.com/paper/2105.06118/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2021.

## Built on by (most cited first, 3 of 3)

- [Bandit Submodular Maximization for Multi-Robot Coordination in Unpredictable and Partially Observable Environments](https://mlsanity.com/paper/2305.12795/) (2023)
- [Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation](https://mlsanity.com/paper/2306.06192/) (2023)
- [Leveraging Untrustworthy Commands for Multi-Robot Coordination in Unpredictable Environments: A Bandit Submodular Maximization Approach](https://mlsanity.com/paper/2309.16161/) (2023)

## Abstract (arXiv)

Heterogeneous multi-robot systems are advantageous for operations in unknown environments because functionally specialised robots can gather environmental information, while others perform tasks. We define this decomposition as the scout-task robot architecture and show how it avoids the need to explicitly balance exploration and exploitation~by permitting the system to do both simultaneously. The challenge is to guide exploration in a way that improves overall performance for time-limited tasks. We derive a novel upper confidence bound for simultaneous exploration and exploitation based on mutual information and present a general solution for scout-task coordination using decentralised Monte Carlo tree search. We evaluate the performance of our algorithms in a multi-drone surveillance scenario in which scout robots are equipped with low-resolution, long-range sensors and task robots capture detailed information using short-range sensors. The results address a new class of coordination problem for heterogeneous teams that has many practical applications.