# Safety Assurances for Human-Robot Interaction via Confidence-aware Game-theoretic Human Models

arXiv: https://arxiv.org/abs/2109.14700 · HTML: https://mlsanity.com/paper/2109.14700/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2021.

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

- [Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis](https://mlsanity.com/paper/2312.08782/) (2023)
- [The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems](https://mlsanity.com/paper/2309.05837/) (2023)
- [Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving](https://mlsanity.com/paper/2407.00959/) (2024)
- [Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach](https://mlsanity.com/paper/2302.00171/) (2023)
- [Task-Relevant Failure Detection for Trajectory Predictors in Autonomous Vehicles](https://mlsanity.com/paper/2207.12380/) (2022)
- [Towards Modeling and Influencing the Dynamics of Human Learning](https://mlsanity.com/paper/2301.00901/) (2023)
- [InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot Actions](https://mlsanity.com/paper/2311.12943/) (2023)
- [Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach](https://mlsanity.com/paper/2202.07720/) (2022)
- [Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy](https://mlsanity.com/paper/2309.01267/) (2023)
- [Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environments](https://mlsanity.com/paper/2201.01347/) (2022)
- [Human-AI Safety: A Descendant of Generative AI and Control Systems Safety](https://mlsanity.com/paper/2405.09794/) (2024)
- [Predictive Planner for Autonomous Driving with Consistency Models](https://mlsanity.com/paper/2502.08033/) (2025)
- [Blending Data-Driven Priors in Dynamic Games](https://mlsanity.com/paper/2402.14174/) (2024)
- [Learning Latent Representations to Co-Adapt to Humans](https://mlsanity.com/paper/2212.09586/) (2022)
- [Socratic: Enhancing Human Teamwork via AI-enabled Coaching](https://mlsanity.com/paper/2502.17643/) (2025)
- [Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review](https://mlsanity.com/paper/2307.08024/) (2023)
- [Who Plays First? Optimizing the Order of Play in Stackelberg Games with Many Robots](https://mlsanity.com/paper/2402.09246/) (2024)
- [Context-Based Echo State Networks with Prediction Confidence for Human-Robot Shared Control](https://mlsanity.com/paper/2412.00541/) (2024)
- [A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach](https://mlsanity.com/paper/2512.11944/) (2025)
- [Learned Tree Search for Long-Horizon Social Robot Navigation in Shared Airspace](https://mlsanity.com/paper/2304.01428/) (2023)
- [Heterogeneous Decision Making in Mixed Traffic: Uncertainty-aware Planning and Bounded Rationality](https://mlsanity.com/paper/2502.18529/) (2025)

## Abstract (arXiv)

An outstanding challenge with safety methods for human-robot interaction is reducing their conservatism while maintaining robustness to variations in human behavior. In this work, we propose that robots use confidence-aware game-theoretic models of human behavior when assessing the safety of a human-robot interaction. By treating the influence between the human and robot as well as the human's rationality as unobserved latent states, we succinctly infer the degree to which a human is following the game-theoretic interaction model. We leverage this model to restrict the set of feasible human controls during safety verification, enabling the robot to confidently modulate the conservatism of its safety monitor online. Evaluations in simulated human-robot scenarios and ablation studies demonstrate that imbuing safety monitors with confidence-aware game-theoretic models enables both safe and efficient human-robot interaction. Moreover, evaluations with real traffic data show that our safety monitor is less conservative than traditional safety methods in real human driving scenarios.