# Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning

arXiv: https://arxiv.org/abs/1801.04099 · HTML: https://mlsanity.com/paper/1801.04099/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2018.

## Built on by (most cited first, 30 of 32)

- [On the Importance of Environments in Human-Robot Coordination](https://mlsanity.com/paper/2106.10853/) (2021)
- [Human-robot collaboration and machine learning: a systematic review of recent research](https://mlsanity.com/paper/2110.07448/) (2021)
- [Conditional Imitation Learning for Multi-Agent Games](https://mlsanity.com/paper/2201.01448/) (2022)
- [Towards Modeling and Influencing the Dynamics of Human Learning](https://mlsanity.com/paper/2301.00901/) (2023)
- [Surrogate Assisted Generation of Human-Robot Interaction Scenarios](https://mlsanity.com/paper/2304.13787/) (2023)
- [Robot Interaction Behavior Generation based on Social Motion Forecasting for Human-Robot Interaction](https://mlsanity.com/paper/2402.04768/) (2024)
- [Trust-Aware Planning: Modeling Trust Evolution in Iterated Human-Robot Interaction](https://mlsanity.com/paper/2105.01220/) (2021)
- [Motivating Physical Activity via Competitive Human-Robot Interaction](https://mlsanity.com/paper/2202.07068/) (2022)
- [Utility Theory based Cognitive Modeling in the Application of Robotics: A Survey](https://mlsanity.com/paper/2306.09445/) (2023)
- [Nature's Insight: A Novel Framework and Comprehensive Analysis of Agentic Reasoning Through the Lens of Neuroscience](https://mlsanity.com/paper/2505.05515/) (2025)
- [Recursively-Constrained Partially Observable Markov Decision Processes](https://mlsanity.com/paper/2310.09688/) (2023)
- [Explainable Interface for Human-Autonomy Teaming: A Survey](https://mlsanity.com/paper/2405.02583/) (2024)
- [Inductive Learning of Robot Task Knowledge from Raw Data and Online Expert Feedback](https://mlsanity.com/paper/2501.07507/) (2025)
- [Enforcing Cybersecurity Constraints for LLM-driven Robot Agents for Online Transactions](https://mlsanity.com/paper/2503.15546/) (2025)
- [TDM: Trustworthy Decision-Making via Interpretability Enhancement](https://mlsanity.com/paper/2108.06080/) (2021)
- [Selectively Providing Reliance Calibration Cues With Reliance Prediction](https://mlsanity.com/paper/2302.09995/) (2023)
- [Robust Robot Planning for Human-Robot Collaboration](https://mlsanity.com/paper/2302.13916/) (2023)
- [TGLD: A Trust-Aware Game-Theoretic Lane-Changing Decision Framework for Automated Vehicles in Heterogeneous Traffic](https://mlsanity.com/paper/2507.10075/) (2025)
- [Intuitive Programming, Adaptive Task Planning, and Dynamic Role Allocation in Human-Robot Collaboration](https://mlsanity.com/paper/2511.08732/) (2025)
- [Towards Formalizing HRI Data Collection Processes](https://mlsanity.com/paper/2203.08396/) (2022)
- [CAIS-DMA: A Decision-Making Assistant for Collaborative AI Systems](https://mlsanity.com/paper/2311.04562/) (2023)
- [Dynamic Explanation Emphasis in Human-XAI Interaction with Communication Robot](https://mlsanity.com/paper/2403.14550/) (2024)
- [Predicting Trust Dynamics with Dynamic SEM in Human-AI Cooperation](https://mlsanity.com/paper/2407.01752/) (2024)
- [Open Human-Robot Collaboration using Decentralized Inverse Reinforcement Learning](https://mlsanity.com/paper/2410.01790/) (2024)
- [Minimax-optimal trust-aware multi-armed bandits](https://mlsanity.com/paper/2410.03651/) (2024)
- [Improving Trust Estimation in Human-Robot Collaboration Using Beta Reputation at Fine-grained Timescales](https://mlsanity.com/paper/2411.01866/) (2024)
- [Task load dependent decision referrals for joint binary classification in human-automation teams](https://mlsanity.com/paper/2504.04248/) (2025)
- [Learning to Trust: Bayesian Adaptation to Varying Suggester Reliability in Sequential Decision Making](https://mlsanity.com/paper/2511.12378/) (2025)
- [C-IDS: Solving Contextual POMDP via Information-Directed Objective](https://mlsanity.com/paper/2602.03939/) (2026)
- [Leveraging the Value of Information in POMDP Planning](https://mlsanity.com/paper/2604.01434/) (2026)

## Abstract (arXiv)

Trust in autonomy is essential for effective human-robot collaboration and user adoption of autonomous systems such as robot assistants. This paper introduces a computational model which integrates trust into robot decision-making. Specifically, we learn from data a partially observable Markov decision process (POMDP) with human trust as a latent variable. The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human trust, and (iii) choose actions that maximize team performance over the long term. We validated the model through human subject experiments on a table-clearing task in simulation (201 participants) and with a real robot (20 participants). In our studies, the robot builds human trust by manipulating low-risk objects first. Interestingly, the robot sometimes fails intentionally in order to modulate human trust and achieve the best team performance. These results show that the trust-POMDP calibrates trust to improve human-robot team performance over the long term. Further, they highlight that maximizing trust alone does not always lead to the best performance.