# Robot Capability and Intention in Trust-based Decisions across Tasks

arXiv: https://arxiv.org/abs/1909.05329 · HTML: https://mlsanity.com/paper/1909.05329/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communication](https://mlsanity.com/paper/2203.02877/) (2022)
- [Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration](https://mlsanity.com/paper/2103.04077/) (2021)
- [SCALES: From Fairness Principles to Constrained Decision-Making](https://mlsanity.com/paper/2209.10860/) (2022)
- [Human and AI Trust: Trust Attitude Measurement Instrument](https://mlsanity.com/paper/2510.21535/) (2025)
- [What Are You Doing? Effects of Intermediate Feedback from Agentic LLM In-Car Assistants During Multi-Step Processing](https://mlsanity.com/paper/2602.15569/) (2026)
- [When Robots Say No: Temporal Trust Recovery Through Explanation](https://mlsanity.com/paper/2510.21716/) (2025)

## Abstract (arXiv)

In this paper, we present results from a human-subject study designed to explore two facets of human mental models of robots---inferred capability and intention---and their relationship to overall trust and eventual decisions. In particular, we examine delegation situations characterized by uncertainty, and explore how inferred capability and intention are applied across different tasks. We develop an online survey where human participants decide whether to delegate control to a simulated UAV agent. Our study shows that human estimations of robot capability and intent correlate strongly with overall self-reported trust. However, overall trust is not independently sufficient to determine whether a human will decide to trust (delegate) a given task to a robot. Instead, our study reveals that estimations of robot intention, capability, and overall trust are integrated when deciding to delegate. From a broader perspective, these results suggest that calibrating overall trust alone is insufficient; to make correct decisions, humans need (and use) multi-faceted mental models when collaborating with robots across multiple contexts.