# On the Utility of Model Learning in HRI

arXiv: https://arxiv.org/abs/1901.01291 · HTML: https://mlsanity.com/paper/1901.01291/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2019.

## Built on by (most cited first, 21 of 21)

- [Collaborating with Humans without Human Data](https://mlsanity.com/paper/2110.08176/) (2021)
- [Large Language Models as Zero-Shot Human Models for Human-Robot Interaction](https://mlsanity.com/paper/2303.03548/) (2023)
- [Hard Choices in Artificial Intelligence](https://mlsanity.com/paper/2106.11022/) (2021)
- [Evaluating the Robustness of Collaborative Agents](https://mlsanity.com/paper/2101.05507/) (2021)
- [Adaptive Coordination in Social Embodied Rearrangement](https://mlsanity.com/paper/2306.00087/) (2023)
- [Deep Interpretable Models of Theory of Mind](https://mlsanity.com/paper/2104.02938/) (2021)
- [A Survey on Reinforcement Learning Methods in Character Animation](https://mlsanity.com/paper/2203.04735/) (2022)
- [The Overcooked Generalisation Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Design](https://mlsanity.com/paper/2406.17949/) (2024)
- [On complementing end-to-end human behavior predictors with planning](https://mlsanity.com/paper/2103.05661/) (2021)
- [Optimal Behavior Prior: Data-Efficient Human Models for Improved Human-AI Collaboration](https://mlsanity.com/paper/2211.01602/) (2022)
- [MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communication](https://mlsanity.com/paper/2203.02877/) (2022)
- [Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data](https://mlsanity.com/paper/2103.04289/) (2021)
- [Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation](https://mlsanity.com/paper/2401.00006/) (2023)
- [When the Inference Meets the Explicitness or Why Multimodality Can Make Us Forget About the Perfect Predictor](https://mlsanity.com/paper/2602.18850/) (2026)
- [CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction](https://mlsanity.com/paper/2308.14965/) (2023)
- [Anticipatory Planning: Improving Long-Lived Planning by Estimating Expected Cost of Future Tasks](https://mlsanity.com/paper/2305.04692/) (2023)
- [Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization](https://mlsanity.com/paper/2310.07218/) (2023)
- [Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning](https://mlsanity.com/paper/2312.15160/) (2023)
- [Goal Recognition Design for General Behavioral Agents using Machine Learning](https://mlsanity.com/paper/2404.03054/) (2024)
- [Incorporating Rivalry in Reinforcement Learning for a Competitive Game](https://mlsanity.com/paper/2208.10327/) (2022)
- [On the Utility of Accounting for Human Beliefs about AI Intention in Human-AI Collaboration](https://mlsanity.com/paper/2406.06051/) (2024)

## Abstract (arXiv)

Fundamental to robotics is the debate between model-based and model-free learning: should the robot build an explicit model of the world, or learn a policy directly? In the context of HRI, part of the world to be modeled is the human. One option is for the robot to treat the human as a black box and learn a policy for how they act directly. But it can also model the human as an agent, and rely on a "theory of mind" to guide or bias the learning (grey box). We contribute a characterization of the performance of these methods for an autonomous driving task under the optimistic case of having an ideal theory of mind, as well as under different scenarios in which the assumptions behind the robot's theory of mind for the human are wrong, as they inevitably will be in practice.