# A Framework for Adapting Human-Robot Interaction to Diverse User Groups

arXiv: https://arxiv.org/abs/2410.11377 · HTML: https://mlsanity.com/paper/2410.11377/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Chat with the Environment: Interactive Multimodal Perception Using Large Language Models](https://mlsanity.com/paper/2303.08268/) (2023)
- [Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics](https://mlsanity.com/paper/2011.09756/) (2020)
- [LaMI: Large Language Models for Multi-Modal Human-Robot Interaction](https://mlsanity.com/paper/2401.15174/) (2024)
- [Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Models](https://mlsanity.com/paper/2309.04316/) (2023)
- [Improved Trust in Human-Robot Collaboration with ChatGPT](https://mlsanity.com/paper/2304.12529/) (2023)
- [Details Make a Difference: Object State-Sensitive Neurorobotic Task Planning](https://mlsanity.com/paper/2406.09988/) (2024)
- [The Robot Household Marathon Experiment](https://mlsanity.com/paper/2011.09792/) (2020)
- [REX: Designing User-centered Repair and Explanations to Address Robot Failures](https://mlsanity.com/paper/2405.16710/) (2024)
- [A Near-Real-Time Processing Ego Speech Filtering Pipeline Designed for Speech Interruption During Human-Robot Interaction](https://mlsanity.com/paper/2405.13477/) (2024)

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

- [Large Language Model Data Generation for Enhanced Intent Recognition in German Speech](https://mlsanity.com/paper/2508.06277/) (2025)

## Abstract (arXiv)

To facilitate natural and intuitive interactions with diverse user groups in real-world settings, social robots must be capable of addressing the varying requirements and expectations of these groups while adapting their behavior based on user feedback. While previous research often focuses on specific demographics, we present a novel framework for adaptive Human-Robot Interaction (HRI) that tailors interactions to different user groups and enables individual users to modulate interactions through both minor and major interruptions. Our primary contributions include the development of an adaptive, ROS-based HRI framework with an open-source code base. This framework supports natural interactions through advanced speech recognition and voice activity detection, and leverages a large language model (LLM) as a dialogue bridge. We validate the efficiency of our framework through module tests and system trials, demonstrating its high accuracy in age recognition and its robustness to repeated user inputs and plan changes.