# Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks

arXiv: https://arxiv.org/abs/2007.12803 · HTML: https://mlsanity.com/paper/2007.12803/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2020.

## Built on by (most cited first, 15 of 15)

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- [Alexa Arena: A User-Centric Interactive Platform for Embodied AI](https://mlsanity.com/paper/2303.01586/) (2023)
- [Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization](https://mlsanity.com/paper/2306.09299/) (2023)
- [GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment](https://mlsanity.com/paper/2403.11075/) (2024)
- [Explainable Autonomous Robots: A Survey and Perspective](https://mlsanity.com/paper/2105.02658/) (2021)
- [On the Emergence of Symmetrical Reality](https://mlsanity.com/paper/2401.15132/) (2024)
- [Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges](https://mlsanity.com/paper/2112.04698/) (2021)
- [A Mental-Model Centric Landscape of Human-AI Symbiosis](https://mlsanity.com/paper/2202.09447/) (2022)
- [Inverse Attention Agents for Multi-Agent Systems](https://mlsanity.com/paper/2410.21794/) (2024)
- [Exploring Causality for HRI: A Case Study on Robotic Mental Well-being Coaching](https://mlsanity.com/paper/2503.11684/) (2025)
- [Building Mental Models through Preview of Autopilot Behaviors](https://mlsanity.com/paper/2104.05470/) (2021)
- [Effective Explanations for Belief-Desire-Intention Robots: When and What to Explain](https://mlsanity.com/paper/2507.02016/) (2025)
- [DPMT: Dual Process Multi-scale Theory of Mind Framework for Real-time Human-AI Collaboration](https://mlsanity.com/paper/2507.14088/) (2025)

## Abstract (arXiv)

Human collaborators can effectively communicate with their partners to finish a common task by inferring each other's mental states (e.g., goals, beliefs, and desires). Such mind-aware communication minimizes the discrepancy among collaborators' mental states, and is crucial to the success in human ad-hoc teaming. We believe that robots collaborating with human users should demonstrate similar pedagogic behavior. Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the user's mental state. To evaluate our framework, we conduct a user study on a real-time human-robot cooking task. Experimental results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot. Code and video demos are available on our project website: https://xfgao.github.io/xCookingWeb/.