Sidharth Vasudev

About

Sidharth Vasudev is a robotics researcher whose work centers on human-robot interaction, assistive feeding, and contextual bandit learning. His most-cited paper, "To Ask or not to Ask: Human-in-the-loop Contextual Bandits with Applications in Robot-Assisted Feeding" (2025, 4 citations), tackles a critical challenge in assistive robotics: enabling robots to adapt to diverse, unpredictable food items during bite acquisition. Rather than relying solely on fully autonomous strategies that struggle to generalize across varying shapes, textures, and sizes, Vasudev proposes a human-in-the-loop framework that strategically queries the care recipient for feedback when encountering novel items. This approach balances autonomy with human guidance, improving efficiency and safety in real-world feeding scenarios. His work directly addresses the needs of individuals with motor impairments, aiming to restore dignity and independence through intelligent robotic assistance. By integrating bandit algorithms with user feedback, Vasudev contributes to a growing body of research on adaptive, socially-aware robots. His findings have implications for broader assistive technologies, where robots must navigate uncertainty while respecting human preferences and limitations.

Research Focus

Key Achievements

Top Papers

1. 1
   To Ask or not to Ask: Human-in-the-loop Contextual Bandits with Applications in Robot-Assisted Feeding

   4 citations · 2025

Key Collaborators

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