Bio

Rebecca Ramnauth is a postdoctoral researcher in the Yale Social Robotics Laboratory, working with Brian Scassellati. Her research lies at the intersection of socially assistive robotics, human–robot interaction, and artificial intelligence. Her doctoral work centered on socially assistive robotics, with a focus on long-term, real-world deployments supporting social and emotional regulation in autism interventions. Her contributions have been recognized with the NSF Graduate Research Fellowship and the National Academies Ford Foundation Fellowship, as well as multiple Best Paper Awards at premier conferences, including IEEE/ACM HRI (2021, 2022, 2025) and IEEE RO-MAN (2025). Before her Ph.D., she served as Assistant Dean of Research at Long Island University and taught as an adjunct professor at several universities, bringing administrative and teaching expertise to her academic career.

Designing, Developing, and Deploying Robots for Social Therapy

Neurodevelopmental conditions present a growing clinical reality. In the United States, autism prevalence has risen from 1 in 150 children two decades ago to 1 in 36 today, and more broadly, 1 in 6 children are diagnosed with a developmental disability. These conditions encompass a broad spectrum of cognitive profiles, making effective care highly individualized, resource-intensive, and difficult to scale. This complexity presents a valuable testbed for developing adaptive, socially grounded, clinically meaningful technologies.

My research posits that robotics holds unique promise for augmenting therapeutic efforts. Robots possess a physical presence—they can turn to face you, wait for your reply, and hold space in ways that organically elicit social engagement. This enables on-demand, personalized support that is socially grounded and physically co-present. However, designing robots capable of delivering effective therapy requires progress across three frontiers: design (How should the system look and interact with users?), development (How can we technically realize these choices for robust, real-world use?), and deployment (How effectively does the system work in real-world contexts with real users?).

My work advances robotics across these three frontiers. First, I design therapies targeting social and emotional skills such as joint attention, conversational reciprocity, deep breathing, and de-escalation. Second, I address technical challenges of autonomous interaction, including disambiguating social presence, discerning when to engage, and safeguarding foundation-model-driven actions. Finally, I conduct deployments in homes and schools lasting weeks to months. These studies establish several landmark firsts: (1) the first robotic interventions tailored for adults with autism; (2) the first evidence of sustained therapeutic efficacy from extended robot use; (3) the first integrations of foundation models for adaptable, improvised therapies; (4) the first robot-led de-escalation interventions in public spaces; and (5) the first multi-user, personalized therapies agnostic to age and diagnostic profile.