# Learning Task Requirements and Agent Capabilities for Multi-agent Task Allocation

arXiv: https://arxiv.org/abs/2211.03286 · HTML: https://mlsanity.com/paper/2211.03286/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2022.

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

- [Acting on the Unseen: Communication-Free Collaborative Filtering for Decentralized Multi-Robot Task Allocation](https://mlsanity.com/paper/2605.25584/) (2026)

## Abstract (arXiv)

This paper presents a learning framework to estimate an agent capability and task requirement model for multi-agent task allocation. With a set of team configurations and the corresponding task performances as the training data, linear task constraints can be learned to be embedded in many existing optimization-based task allocation frameworks. Comprehensive computational evaluations are conducted to test the scalability and prediction accuracy of the learning framework with a limited number of team configurations and performance pairs. A ROS and Gazebo-based simulation environment is developed to validate the proposed requirements learning and task allocation framework in practical multi-agent exploration and manipulation tasks. Results show that the learning process for scenarios with 40 tasks and 6 types of agents uses around 12 seconds, ending up with prediction errors in the range of 0.5-2%.