ΑΙhub.org
 

Generations in Dialogue: Human-robot interactions and social robotics with Professor Marynel Vasquez

Generations in Dialogue: Bridging Perspectives in AI is a podcast from AAAI featuring thought-provoking discussions between AI experts, practitioners, and enthusiasts from different age groups and backgrounds. Each episode delves into how generational experiences shape views on AI, exploring the challenges, opportunities, and ethical considerations that come with the advancement of this transformative technology.

Human-robot interactions and social robotics with Professor Marynel Vázquez

In the fourth episode of this new series from AAAI, host Ella Lan chats to Professor Marynel Vázquez about what inspired her research direction, how her perspective on human-robot interactions has changed over time, robots navigating the social world, potential for using robots in education, modeling interactions as graphs, addressing misunderstandings with regards to robots in society, getting input from target users, the challenge of recognising when errors happen, making robots that adapt, and more.

About Professor Marynel Vázquez:

Marynel Vázquez is a computer scientist and roboticist whose research focuses on Human-Robot Interaction (HRI), particularly in multi-party settings. She studies social group dynamics—such as spatial behavior and social influence—in HRI, and develops perception and decision-making algorithms that enable autonomous, socially aware robot behavior. A central theme in her work is modeling interactions as graphs, allowing robots to reason about individuals, relationships, and groups simultaneously. Her interdisciplinary approach combines computer science, behavioral science, and design, and she enjoys building new robotic systems and research infrastructure to bring theoretical ideas into real-world practice.

About the host

Ella Lan, a member of the AAAI Student Committee, is the host of “Generations in Dialogue: Bridging Perspectives in AI.” She is passionate about bringing together voices across career stages to explore the evolving landscape of artificial intelligence. Ella is a student at Stanford University tentatively studying Computer Science and Psychology, and she enjoys creating spaces where technical innovation intersects with ethical reflection, human values, and societal impact. Her interests span education, healthcare, and AI ethics, with a focus on building inclusive, interdisciplinary conversations that shape the future of responsible AI.



tags:


Association for the Understanding of Artificial Intelligence (AAAI)

            AIhub is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

A multi-armed robot for assisting with agricultural tasks

and   27 Mar 2026
How can a robot safely manipulate branches to reveal hidden flowers while remaining aware of interaction forces and minimizing damage?

Resource-constrained image generation and visual understanding: an interview with Aniket Roy

  26 Mar 2026
Aniket tells us about his research exploring how modern generative models can be adapted to operate efficiently while maintaining strong performance.

RWDS Big Questions: how do we highlight the role of statistics in AI?

  25 Mar 2026
Next in our series, the panel explores the statistical underpinning of AI.

A history of RoboCup with Manuela Veloso

  24 Mar 2026
Find out how RoboCup got started and how the competition has evolved, from one of the co-founders.

Information-driven design of imaging systems

  23 Mar 2026
Framework that enables direct evaluation and optimization of imaging systems based on their information content.

Machine learning framework to predict global imperilment status of freshwater fish

  20 Mar 2026
“With our model, decision makers can deploy resources in advance before a species becomes imperiled.”

Interview with AAAI Fellow Yan Liu: machine learning for time series

  19 Mar 2026
Hear from 2026 AAAI Fellow Yan Liu about her research into time series, the associated applications, and the promise of physics-informed models.

A principled approach for data bias mitigation

  18 Mar 2026
Find out more about work presented at AIES 2025 which proposes a new way to measure data bias, along with a mitigation algorithm with mathematical guarantees.



AIhub is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence