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:



Related posts :



What are small language models and how do they differ from large ones?

  06 Jan 2026
Let’s explore what makes SLMs and LLMs different – and how to choose the right one for your situation.

Forthcoming machine learning and AI seminars: January 2026 edition

  05 Jan 2026
A list of free-to-attend AI-related seminars that are scheduled to take place between 5 January and 28 February 2026.

AAAI presidential panel – AI perception versus reality video discussion

  02 Jan 2026
Watch the second panel discussion in this series from AAAI.

More than half of new articles on the internet are being written by AI

  31 Dec 2025
The line between human and machine authorship is blurring, particularly as it’s become increasingly difficult to tell whether something was written by a person or AI.
monthly digest

2025 digest of digests

  30 Dec 2025
We look back through the archives of our monthly digests to pick out some highlights from the year.
monthly digest

AIhub monthly digest: December 2025 – studying bias in AI-based recruitment tools, an image dataset for ethical AI benchmarking, and end of year com

  29 Dec 2025
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.

Half of UK novelists believe AI is likely to replace their work entirely

  24 Dec 2025
A new report asks literary creatives about their views on generative AI tools and LLM-authored books.

RL without TD learning

  23 Dec 2025
This post introduces a reinforcement learning algorithm based on a divide and conquer paradigm.



 

AIhub is supported by:






 












©2025.05 - Association for the Understanding of Artificial Intelligence