# Using Design Metaphors to Understand User Expectations of Socially Interactive Robot Embodiments

arXiv: https://arxiv.org/abs/2201.10671 · HTML: https://mlsanity.com/paper/2201.10671/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2022.

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

- [Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation](https://mlsanity.com/paper/2501.01367/) (2025)
- [Improving User Experience in Preference-Based Optimization of Reward Functions for Assistive Robots](https://mlsanity.com/paper/2411.11182/) (2024)
- [Singing the Body Electric: The Impact of Robot Embodiment on User Expectations](https://mlsanity.com/paper/2401.06977/) (2024)
- [Improving through Interaction: Searching Behavioral Representation Spaces with CMA-ES-IG](https://mlsanity.com/paper/2603.09011/) (2026)
- [Operationalizing Perceptions of Agent Gender: Foundations and Guidelines](https://mlsanity.com/paper/2603.26682/) (2026)
- [AI Watchdog: Agent Interfaces for Detecting and Defending Against Manipulative Dark Patterns in AI Conversations](https://mlsanity.com/paper/2608.21841/) (2026)

## Abstract (arXiv)

The physical design of a robot suggests expectations of that robot's functionality for human users and collaborators. When those expectations align with the true capabilities of the robot, interaction with the robot is enhanced. However, misalignment of those expectations can result in an unsatisfying interaction. This paper uses Mechanical Turk to evaluate user expectation through the use of design metaphors as applied to a wide range of robot embodiments. The first study (N=382) associates crowd-sourced design metaphors to different robot embodiments. The second study (N=803) assesses initial social expectations of robot embodiments. The final study (N=805) addresses the degree of abstraction of the design metaphors and the functional expectations projected on robot embodiments. Together, these results can guide robot designers toward aligning user expectations with true robot capabilities, facilitating positive human-robot interaction.