# AI-HRI Brings New Dimensions to Human-Aware Design for Human-Aware AI

arXiv: https://arxiv.org/abs/2210.11832 · HTML: https://mlsanity.com/paper/2210.11832/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [The Lovelace 2.0 Test of Artificial Creativity and Intelligence](https://mlsanity.com/paper/1410.6142/) (2014)
- [Challenges of Human-Aware AI Systems](https://mlsanity.com/paper/1910.07089/) (2019)
- [A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction](https://mlsanity.com/paper/2109.12912/) (2021)
- [MuMMER: Socially Intelligent Human-Robot Interaction in Public Spaces](https://mlsanity.com/paper/1909.06749/) (2019)
- [A Knowledge Driven Approach to Adaptive Assistance Using Preference Reasoning and Explanation](https://mlsanity.com/paper/2012.02904/) (2020)
- [Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program](https://mlsanity.com/paper/1702.00137/) (2017)
- [Responsive Planning and Recognition for Closed-Loop Interaction](https://mlsanity.com/paper/1909.06427/) (2019)
- [Supporting User Autonomy with Multimodal Fusion to Detect when a User Needs Assistance from a Social Robot](https://mlsanity.com/paper/2012.04078/) (2020)
- [Using Pupil Diameter to Measure Cognitive Load](https://mlsanity.com/paper/1812.07653/) (2018)
- [Towards a Conversational Measure of Trust](https://mlsanity.com/paper/2010.04885/) (2020)
- [Towards Preference Learning for Autonomous Ground Robot Navigation Tasks](https://mlsanity.com/paper/2010.16361/) (2020)

## Abstract (arXiv)

Since the first AI-HRI held at the 2014 AAAI Fall Symposium Series, a lot of the presented research and discussions have emphasized how artificial intelligence (AI) developments can benefit human-robot interaction (HRI). This portrays HRI as an application, a source of domain-specific problems to solve, to the AI community. Likewise, this portrays AI as a tool, a source of solutions available for relevant problems, to the HRI community. However, members of the AI-HRI research community will point out that the relationship has a deeper synergy than matchmaking problems and solutions -- there are insights from each field that impact how the other one thinks about the world and performs scientific research. There is no greater opportunity for sharing perspectives at the moment than human-aware AI, which studies how to account for the fact that people are more than a source of data or part of an algorithm. We will explore how AI-HRI can change the way researchers think about human-aware AI, from observation through validation, to make even the algorithmic design process human-aware.