Shivendra Agrawal

CAIRO Lab @ CU Boulder

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University of Colorado

Boulder, Colorado

About myself: Ph.D. Candidate in Computer Science developing context-aware human-centered AI for real-world robotics. My research bridges Robotics, HRI, and Embodied AI to create context-aware systems that interpret semantic, social, and geometric cues. Technical expertise spans computer vision deployment, full-stack system architecture, and human-subject evaluation, leveraging modern techniques from probabilistic planning to Foundation Models (VLMs) for deployable embodied AI. Proven track record of publishing in top-tier venues (HRI, AAMAS, IROS). Award-winning educator and dedicated mentor, recognized for instructional excellence with multiple Outstanding TA and Instructor awards.

Other interests: I enjoy playing tennis a lot (like a lot!!). On good sunny days, I also like to bike and run in the lovely city of Boulder. (Bonus) Boulder Creek Path Fall view through my bike. I also like to explore local food and beverages.

Minor-flex - I have more than 60 millions views on my Google Map contributions and have received a rather vibrant pair of socks from Google. And no, that was all I ever got from them.

Summary of some of my work:

  • Explainable Robotic Coaching: A framework for a robot to infer a human partner’s likely mental model by observing suboptimal actions to provide targeted coaching with justifications (HRI ‘19 Best Paper Runner-up). Project page

  • Designing Embodied AI for Social Context: Understanding that human environments are governed by social norms, I developed a perceptive robotic cane that used models from psychology to identify socially appropriate seating locations (optimizing for privacy and intimacy), deploying all perception locally on the edge. Project page

  • Learning Decision-Making Policies from Human Behavior: My “ShelfHelp” system learned an optimal guidance policy from human behavioral data to provide manipulation guidance (featured in national media). Project page

  • Robust Semantic Localization: Traditional methods falter in geometrically ambiguous (like retail aisles) environments. “ShelfAware” leverages semantic features within a probabilistic framework to handle quasi-static changes. It does global localization using onboard RGB-D/VIO sensors, eliminating the need for environmental augmentation. Project page

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