Winter Robotics Colloquium: Marynel Vázquez (Yale University)

Why It Matters

Understanding and engineering social context is essential for robots to operate safely and effectively alongside humans, directly influencing the commercial viability of next‑generation autonomous assistants.

Key Takeaways

• Generalist robots require both physical and social intelligence.
• Social context includes environment, agents, and their relationships.
• Large language models can expand robot understanding of nuanced cues.
• Experiments show robots can influence human behavior through vulnerability.
• Group dynamics amplify robot-mediated social influence in HRI.

Summary

In this Winter Robotics Colloquium, Marynel Vázquez from Yale University argues that the next wave of generalist robots must combine physical dexterity with social intelligence. Using the household robot Rosie as a touchstone, she illustrates how today’s manipulation‑focused systems are evolving toward agents that can interpret language, gestures, and contextual cues in real‑world settings such as homes, factories, and elder‑care facilities. Vázquez proposes a concrete definition of "social context" as the set of attributes of agents, environments, and their inter‑relationships that shape interaction outcomes. She highlights the massive combinatorial space of these factors, noting that unlike autonomous vehicles, human‑robot interaction lacks a clear rulebook, making long‑tail, rare events a central challenge. Large language models, she suggests, offer a pathway to endow robots with the nuanced understanding required for these open‑ended scenarios. The talk’s most vivid evidence comes from a series of lab experiments on robot abuse and social influence. When a robot displayed vulnerability—either by expressing emotion or briefly shutting down—participants were more likely to intervene against a mistreating confederate. In a collaborative task with multiple robots, the group’s collective sadness prompted even stronger human intervention, demonstrating that robots can leverage group dynamics to shape behavior. Vázquez concludes that designing truly generalist robots demands interdisciplinary work spanning robotics, psychology, and AI. As robots become embedded in everyday social spaces, engineers must embed contextual awareness and ethical reasoning, lest they deploy systems that fail in the very human environments they are meant to serve.

Original Description

Title: Beyond Physical Intelligence: Why Generalist Robots Require Social Intelligence

Speaker: Marynel Vázquez (Yale University)

Date: Friday, February 27 2026

Abstract: As the robotics industry moves toward deploying generalist agents in unstructured human environments, such as homes and workplaces, the research focus has largely remained on physical intelligence. While mastering physical tasks is essential, social intelligence is a critical missing piece for widespread technology adoption. To be truly effective, robots must understand, navigate, and manage the nuances of interpersonal interaction. In this talk, I will discuss two aspects of social intelligence that are fundamental for Human-Robot Interaction (HRI). The first one pertains understanding social phenomena that emerges in group interactions and that robots can potentially leverage to navigate complex social situations. The second one is implicit human feedback, i.e., communicative signals that are given off “for free” by humans and that require interpretation. Robots can leverage such implicit feedback to predict how people perceive them and better collaborate with users. Finally, I will reflect on how the latest advancements in machine learning are fundamentally reshaping the way we approach research in Human-Robot Interaction.

Bio: Marynel Vázquez is an Assistant Professor in Yale’s Computer Science Department, where she leads the Interactive Machines Group. Her research investigates fundamental problems in Human-Robot Interaction and Artificial Social Intelligence, often motivated by challenges or opportunities that arise in group human-robot interactions. Marynel received her bachelor's degree in Computer Engineering from Universidad Simón Bolívar in 2008, and obtained her M.S. and Ph.D. in Robotics from Carnegie Mellon University in 2013 and 2017, respectively. Before joining Yale, she was a collaborator of Disney Research and a Post-Doctoral Scholar at the Stanford Vision & Learning Lab. Marynel received a 2024 AFOSR YIP Award, a 2022 NSF CAREER Award, two Amazon Research Awards, and a Google Research Scholar award. Her work has been recognized with a 2025 IJCAI Early Career Spotlight, best paper awards at ACM/IEEE HRI 2023 and IEEE RO-MAN 2022 as well as nominations for paper awards at ACM/IEEE HRI 2021, IEEE IROS 2018, and IEEE RO-MAN 2016.

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