Ruiqi Wang

Ruiqi Wang

R7 On the Beat

Ruiqi Wang

/ Ray-chee Wong /

Hello!

I'm a Ph.D. candidate at Purdue University, where I work in the Smart Machine And Robotics Technology (SMART) Lab with Professor B.C. Min.

My research goal is to enable robots to integrate seamlessly into unstructured, human-centered environments.

To this end, I develop methods for robot learning from human feedback, enabling robots to acquire scalable, aligned, and personalized behaviors without hand-specified objectives. I pursue this goal along three directions:

Eliciting informative feedback How can robots obtain informative feedback with minimal human effort?

My research uses foundation models as feedback priors to reduce human burden, and develops active, context-aware elicitation that decides when, what, and how to ask.

Learning intent-aligned rewards How can robots convert imperfect, multimodal feedback into rewards that faithfully capture human intent?

My research develops robust reward learning methods that distill subtle, noisy, and multimodal feedback into structured representations of human intent.

Sustaining generalizable alignment How can robots remain aligned as tasks, users, and environments change?

My research develops transferable mechanisms that carry learned intent across tasks, users, and environments without drifting from human intent.

Beyond learning from feedback, my research also studies how robots can respond to broader human signals in real time, including physiological state, cognitive workload, trust, and interaction dynamics, to sustain fluent, safe, and effective collaboration.

Research Areas:

Learning from Human Feedback Preference-based Reinforcement Learning Human-Robot Interaction Multimodal Learning Foundation Models for Robotics Multi-Human Multi-Robot Teaming

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2025
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