# Multi-Task Trust Transfer for Human-Robot Interaction

arXiv: https://arxiv.org/abs/1807.01866 · HTML: https://mlsanity.com/paper/1807.01866/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2018.

## Built on by (most cited first, 11 of 11)

- [Large Language Models as Zero-Shot Human Models for Human-Robot Interaction](https://mlsanity.com/paper/2303.03548/) (2023)
- [Embedding Symbolic Temporal Knowledge into Deep Sequential Models](https://mlsanity.com/paper/2101.11981/) (2021)
- [MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communication](https://mlsanity.com/paper/2203.02877/) (2022)
- [Trust-Aware Planning: Modeling Trust Evolution in Iterated Human-Robot Interaction](https://mlsanity.com/paper/2105.01220/) (2021)
- [A Mental Model Based Framework of Trust](https://mlsanity.com/paper/2301.12569/) (2023)
- [Trust-MARL: Trust-Based Multi-Agent Reinforcement Learning Framework for Cooperative On-Ramp Merging Control in Heterogeneous Traffic Flow](https://mlsanity.com/paper/2506.12600/) (2025)
- [The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare](https://mlsanity.com/paper/2411.03287/) (2024)
- [Revisiting Trust in the Era of Generative AI: Factorial Structure and Latent Profiles](https://mlsanity.com/paper/2510.10199/) (2025)
- [ToP-ToM: Trust-aware Robot Policy with Theory of Mind](https://mlsanity.com/paper/2311.04397/) (2023)
- [Boltzmann State-Dependent Rationality](https://mlsanity.com/paper/2404.17725/) (2024)
- [Toward Informed AV Decision-Making: Computational Model of Well-being and Trust in Mobility](https://mlsanity.com/paper/2505.14983/) (2025)

## Abstract (arXiv)

Trust is essential in shaping human interactions with one another and with robots. This paper discusses how human trust in robot capabilities transfers across multiple tasks. We first present a human-subject study of two distinct task domains: a Fetch robot performing household tasks and a virtual reality simulation of an autonomous vehicle performing driving and parking maneuvers. The findings expand our understanding of trust and inspire new differentiable models of trust evolution and transfer via latent task representations: (i) a rational Bayes model, (ii) a data-driven neural network model, and (iii) a hybrid model that combines the two. Experiments show that the proposed models outperform prevailing models when predicting trust over unseen tasks and users. These results suggest that (i) task-dependent functional trust models capture human trust in robot capabilities more accurately, and (ii) trust transfer across tasks can be inferred to a good degree. The latter enables trust-mediated robot decision-making for fluent human-robot interaction in multi-task settings.