# Exploring Causality for HRI: A Case Study on Robotic Mental Well-being Coaching

arXiv: https://arxiv.org/abs/2503.11684 · HTML: https://mlsanity.com/paper/2503.11684/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [Sequence Model Imitation Learning with Unobserved Contexts](https://mlsanity.com/paper/2208.02225/) (2022)
- [Causal Imitation Learning under Temporally Correlated Noise](https://mlsanity.com/paper/2202.01312/) (2022)
- [Causal Reasoning in Simulation for Structure and Transfer Learning of Robot Manipulation Policies](https://mlsanity.com/paper/2103.16772/) (2021)
- [Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks](https://mlsanity.com/paper/2007.12803/) (2020)
- [Why did I fail? A Causal-based Method to Find Explanations for Robot Failures](https://mlsanity.com/paper/2204.04483/) (2022)
- [VITA: A Multi-modal LLM-based System for Longitudinal, Autonomous, and Adaptive Robotic Mental Well-being Coaching](https://mlsanity.com/paper/2312.09740/) (2023)
- [Small but Fair! Fairness for Multimodal Human-Human and Robot-Human Mental Wellbeing Coaching](https://mlsanity.com/paper/2407.01562/) (2024)
- [From Continual Learning to Causal Discovery in Robotics](https://mlsanity.com/paper/2301.03886/) (2023)
- [ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applications](https://mlsanity.com/paper/2402.16068/) (2024)
- [Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios](https://mlsanity.com/paper/2406.04955/) (2024)
- [Participant Perceptions of a Robotic Coach Conducting Positive Psychology Exercises: A Qualitative Analysis](https://mlsanity.com/paper/2209.03827/) (2022)
- [Affective Robotics For Wellbeing: A Scoping Review](https://mlsanity.com/paper/2304.01902/) (2023)
- [Causal Robot Communication Inspired by Observational Learning Insights](https://mlsanity.com/paper/2203.09114/) (2022)
- [An Introduction to Causal Inference Methods for Observational Human-Robot Interaction Research](https://mlsanity.com/paper/2310.20468/) (2023)

## Built on by (most cited first, 2 of 2)

- [Causal Reinforcement Learning based Agent-Patient Interaction with Clinical Domain Knowledge](https://mlsanity.com/paper/2512.00048/) (2025)
- [FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment](https://mlsanity.com/paper/2604.23786/) (2026)

## Abstract (arXiv)

One of the primary goals of Human-Robot Interaction (HRI) research is to develop robots that can interpret human behavior and adapt their responses accordingly. Adaptive learning models, such as continual and reinforcement learning, play a crucial role in improving robots' ability to interact effectively in real-world settings. However, these models face significant challenges due to the limited availability of real-world data, particularly in sensitive domains like healthcare and well-being. This data scarcity can hinder a robot's ability to adapt to new situations. To address these challenges, causality provides a structured framework for understanding and modeling the underlying relationships between actions, events, and outcomes. By moving beyond mere pattern recognition, causality enables robots to make more explainable and generalizable decisions. This paper presents an exploratory causality-based analysis through a case study of an adaptive robotic coach delivering positive psychology exercises over four weeks in a workplace setting. The robotic coach autonomously adapts to multimodal human behaviors, such as facial valence and speech duration. By conducting both macro- and micro-level causal analyses, this study aims to gain deeper insights into how adaptability can enhance well-being during interactions. Ultimately, this research seeks to advance our understanding of how causality can help overcome challenges in HRI, particularly in real-world applications.