# An Integrated Dynamic Method for Allocating Roles and Planning Tasks for Mixed Human-Robot Teams

arXiv: https://arxiv.org/abs/2105.12031 · HTML: https://mlsanity.com/paper/2105.12031/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 3 of 3)

- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [A Hierarchical Architecture for Human-Robot Cooperation Processes](https://mlsanity.com/paper/2009.02807/) (2020)
- [A Human-Centered Dynamic Scheduling Architecture for Collaborative Application](https://mlsanity.com/paper/2103.01831/) (2021)

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

- [Dynamic Human-Robot Role Allocation based on Human Ergonomics Risk Prediction and Robot Actions Adaptation](https://mlsanity.com/paper/2111.03630/) (2021)
- [A Unified Architecture for Dynamic Role Allocation and Collaborative Task Planning in Mixed Human-Robot Teams](https://mlsanity.com/paper/2301.08038/) (2023)
- [Intuitive Programming, Adaptive Task Planning, and Dynamic Role Allocation in Human-Robot Collaboration](https://mlsanity.com/paper/2511.08732/) (2025)

## Abstract (arXiv)

This paper proposes a novel integrated dynamic method based on Behavior Trees for planning and allocating tasks in mixed human robot teams, suitable for manufacturing environments. The Behavior Tree formulation allows encoding a single job as a compound of different tasks with temporal and logic constraints. In this way, instead of the well-studied offline centralized optimization problem, the role allocation problem is solved with multiple simplified online optimization sub-problem, without complex and cross-schedule task dependencies. These sub-problems are defined as Mixed-Integer Linear Programs, that, according to the worker-actions related costs and the workers' availability, allocate the yet-to-execute tasks among the available workers. To characterize the behavior of the developed method, we opted to perform different simulation experiments in which the results of the action-worker allocation and computational complexity are evaluated. The obtained results, due to the nature of the algorithm and to the possibility of simulating the agents' behavior, should describe well also how the algorithm performs in real experiments.