# A Mixed-Integer Linear Programming Formulation for Human Multi-Robot Task Allocation

arXiv: https://arxiv.org/abs/2106.06772 · HTML: https://mlsanity.com/paper/2106.06772/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2021.

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

- [Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments](https://mlsanity.com/paper/2303.14874/) (2023)
- [Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning with Efficient Heuristics](https://mlsanity.com/paper/2306.17609/) (2023)
- [Learning Action Duration and Synergy in Task Planning for Human-Robot Collaboration](https://mlsanity.com/paper/2210.11660/) (2022)
- [Designing faster mixed integer linear programming algorithm via learning the optimal path](https://mlsanity.com/paper/2601.16056/) (2026)

## Abstract (arXiv)

In this work, we address a task allocation problem for human multi-robot settings. Given a set of tasks to perform, we formulate a general Mixed-Integer Linear Programming (MILP) problem aiming at minimizing the overall execution time while optimizing the quality of the executed tasks as well as human and robotic workload. Different skills of the agents, both human and robotic, are taken into account and human operators are enabled to either directly execute tasks or play supervisory roles; moreover, multiple manipulators can tightly collaborate if required to carry out a task. Finally, as realistic in human contexts, human parameters are assumed to vary over time, e.g., due to increasing human level of fatigue. Therefore, online monitoring is required and re-allocation is performed if needed. Simulations in a realistic scenario with two manipulators and a human operator performing an assembly task validate the effectiveness of the approach.