Stefanos Nikolaidi
Biographic Data
| ID | 5127621 |
|---|---|
| NAME | Stefanos Nikolaidi |
| GIVEN NAMES | Stefanos |
| FAMILY NAME | Nikolaidi |
| SIGNATURE | NIKOLAIDI S |
| AFFILIATIONS | University of Southern California |
| ORCID | 0000-0002-8617-3871 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 2 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Designing Robot Identity
Perceptions of gender have a significant impact on human-human interaction, and gender has wide-reaching social implications for robots intended to interact with humans. This work explored two flexible modalities for communicating gender in robots–voice and appearance–and we studied their individual and combined influences on a robot’s perceived gender. We evaluated the perception of a robot’s gender through three online studies. First, we conduc…
Evaluating Human–Robot Interaction Algorithms in Shared Autonomy via Quality Diversity Scenario Generation
The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applications. Exploring diverse scenarios of humans and robots interacting in simulation can improve understanding of the robotic system and avoid potentially costly failures in real-world settings. We formulate this problem as a quality diversity (QD) problem, of which the g…
Design Metaphors for Understanding User Expectations of Socially Interactive Robot Embodiments
The physical design of a robot suggests expectations of that robot’s functionality for human users and collaborators. When those expectations align with the robot’s true capabilities, users are more likely to adopt the technologies for their intended use. However, the relationship between expectations and socially interactive robot design is not well understood. This article applies the concept of design metaphors to robot design and contributes …
Trust-Aware Decision Making for Human-Robot Collaboration
Trust in autonomy is essential for effective human-robot collaboration and user adoption of autonomous systems such as robot assistants. This article introduces a computational model that 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 …
Viewpoint-based legibility optimization
Much robotics research has focused on intent-expressive (legible) motion. However, algorithms that can autonomously generate legible motion have implicitly made the strong assumption of an omniscient observer, with access to the robot's configuration as it changes across time. In reality, human observers have a particular viewpoint, which biases the way they perceive the motion. In this work, we free robots from this assumption and introduce the …
Viewpoint-based legibility optimization
Much robotics research has focused on intent-expressive (legible) motion. However, algorithms that can autonomously generate legible motion have implicitly made the strong assumption of an omniscient observer, with access to the robot's configuration as it changes across time. In reality, human observers have a particular viewpoint, which biases the way they perceive the motion. In this work, we free robots from this assumption and introduce the …
Viewpoint-based legibility optimization
Much robotics research has focused on intent-expressive (legible) motion. However, algorithms that can autonomously generate legible motion have implicitly made the strong assumption of an omniscient observer, with access to the robot's configuration as it changes across time. In reality, human observers have a particular viewpoint, which biases the way they perceive the motion. In this work, we free robots from this assumption and introduce the …
Trust-Aware Decision Making for Human-Robot Collaboration
Trust in autonomy is essential for effective human-robot collaboration and user adoption of autonomous systems such as robot assistants. This article introduces a computational model that 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 …
Evaluating Human–Robot Interaction Algorithms in Shared Autonomy via Quality Diversity Scenario Generation
The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applications. Exploring diverse scenarios of humans and robots interacting in simulation can improve understanding of the robotic system and avoid potentially costly failures in real-world settings. We formulate this problem as a quality diversity (QD) problem, of which the g…
Design Metaphors for Understanding User Expectations of Socially Interactive Robot Embodiments
The physical design of a robot suggests expectations of that robot’s functionality for human users and collaborators. When those expectations align with the robot’s true capabilities, users are more likely to adopt the technologies for their intended use. However, the relationship between expectations and socially interactive robot design is not well understood. This article applies the concept of design metaphors to robot design and contributes …
Designing Robot Identity
Perceptions of gender have a significant impact on human-human interaction, and gender has wide-reaching social implications for robots intended to interact with humans. This work explored two flexible modalities for communicating gender in robots–voice and appearance–and we studied their individual and combined influences on a robot’s perceived gender. We evaluated the perception of a robot’s gender through three online studies. First, we conduc…
Artificial Intelligence (5 works) · Computer Science (5 works) · Human–computer interaction (5 works) · Robot (5 works) · Social Robot Interaction and HRI (3 works) · Autonomy (2 works) · Engineering (2 works) · Human–robot interaction (2 works) · Machine learning (2 works) · Mathematics (2 works)