# A Hierarchical Variable Autonomy Mixed-Initiative Framework for Human-Robot Teaming in Mobile Robotics

arXiv: https://arxiv.org/abs/2211.14095 · HTML: https://mlsanity.com/paper/2211.14095/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 25 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Mixed-Initiative variable autonomy for remotely operated mobile robots](https://mlsanity.com/paper/1911.04848/) (2019)
- [Decision-Making in Driver-Automation Shared Control: A Review and Perspectives](https://mlsanity.com/paper/2007.12597/) (2020)
- [A Bayesian-Based Approach to Human Operator Intent Recognition in Remote Mobile Robot Navigation](https://mlsanity.com/paper/2109.12045/) (2021)
- [Robot Vitals and Robot Health: Towards Systematically Quantifying Runtime Performance Degradation in Robots Under Adverse Conditions](https://mlsanity.com/paper/2207.01684/) (2022)
- [Robot-Assisted Nuclear Disaster Response: Report and Insights from a Field Exercise](https://mlsanity.com/paper/2207.00648/) (2022)
- [VFH+ based shared control for remotely operated mobile robots](https://mlsanity.com/paper/2011.05228/) (2020)
- [Human operator cognitive availability aware Mixed-Initiative control](https://mlsanity.com/paper/2108.11885/) (2021)
- [Fessonia: a Method for Real-Time Estimation of Human Operator Workload Using Behavioural Entropy](https://mlsanity.com/paper/2110.01940/) (2021)

## Built on by (most cited first, 2 of 2)

- [DASH: Deception-Augmented Shared Mental Model for a Human-Machine Teaming System](https://mlsanity.com/paper/2512.18616/) (2025)
- [Dialogue Telemetry: Turn-Level Instrumentation for Autonomous Information Gathering](https://mlsanity.com/paper/2601.09570/) (2026)

## Abstract (arXiv)

This paper presents a Mixed-Initiative (MI) framework for addressing the problem of control authority transfer between a remote human operator and an AI agent when cooperatively controlling a mobile robot. Our Hierarchical Expert-guided Mixed-Initiative Control Switcher (HierEMICS) leverages information on the human operator's state and intent. The control switching policies are based on a criticality hierarchy. An experimental evaluation was conducted in a high-fidelity simulated disaster response and remote inspection scenario, comparing HierEMICS with a state-of-the-art Expert-guided Mixed-Initiative Control Switcher (EMICS) in the context of mobile robot navigation. Results suggest that HierEMICS reduces conflicts for control between the human and the AI agent, which is a fundamental challenge in both the MI control paradigm and also in the related shared control paradigm. Additionally, we provide statistically significant evidence of improved, navigational safety (i.e., fewer collisions), LOA switching efficiency, and conflict for control reduction.