# A Supervised Machine Learning Approach to Operator Intent Recognition for Teleoperated Mobile Robot Navigation

arXiv: https://arxiv.org/abs/2304.14003 · HTML: https://mlsanity.com/paper/2304.14003/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2023.

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

- [Intent-Driven LLM Ensemble Planning for Flexible Multi-Robot Disassembly: Demonstration on EV Batteries](https://mlsanity.com/paper/2510.17576/) (2025)
- [FleetAgent: Teleoperation Assistant for Autonomous Fleets via Vectorized V2N Messages](https://mlsanity.com/paper/2606.21222/) (2026)

## Abstract (arXiv)

In applications that involve human-robot interaction (HRI), human-robot teaming (HRT), and cooperative human-machine systems, the inference of the human partner's intent is of critical importance. This paper presents a method for the inference of the human operator's navigational intent, in the context of mobile robots that provide full or partial (e.g., shared control) teleoperation. We propose the Machine Learning Operator Intent Inference (MLOII) method, which a) processes spatial data collected by the robot's sensors; b) utilizes a supervised machine learning algorithm to estimate the operator's most probable navigational goal online. The proposed method's ability to reliably and efficiently infer the intent of the human operator is experimentally evaluated in realistically simulated exploration and remote inspection scenarios. The results in terms of accuracy and uncertainty indicate that the proposed method is comparable to another state-of-the-art method found in the literature.