Ruiqi Wang

Ruiqi Wang

R7 On the Beat

Ruiqi Wang

Hello!

I’m a Ph.D. candidate at Purdue University, where I work in the Smart Machine And Robotics Technology (SMART) Lab with Professor Byung-Cheol Min.

I am passionate about facilitating the seamless integration of robots into unstructured, human-centered environments: from assistive service in homes to collaborative operations in the field.

To this end, my research centers on developing robot learning methods that enable robots to learn from and adapt to human-centered dynamics across three key dimensions:

  • Capability Heterogeneity: How to optimize human–robot teaming by accounting for inherent yet diverse human capabilities (e.g., cognitive abilities, skill levels, backgrounds) and robot characteristics (e.g., mobility, sensing, autonomy) within specific task contexts?
  • State Uncertainty: How to enable proactive perception and responsiveness to human states (e.g., cognitive load, attention, trust), robot working conditions, and task status that evolve dynamically during operation?
  • Preference Variability: How to align robot interactive patterns with individual preferences through continuous, multimodal human feedback?

Spanning scales from one-to-one human-robot interaction to team-level coordination in multi-human multi-robot teams, my work aims to lay the foundation for a future where robots can naturally understand, adapt to, and collaborate with any human, in any context or situation.

My broad areas of research include:

  • Human-Robot Interaction
  • Human-in-the-Loop Reinforcement Learning
  • Multi-Human Multi-Robot Teaming
  • Foundation Models for Robotics
  • Multimodal Perception and Reasoning

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