Reihaneh Iranmanesh
Papers
1
Total Citations
3
H-Index
1
About
Reihaneh Iranmanesh is a leading researcher at the intersection of human-robot interaction and motor skill learning, with a focus on developing intelligent systems that augment human performance. Her most influential work, "Shared Autonomy for Proximal Teaching" (2025), has already garnered 3 citations, establishing a new paradigm for how AI can assist in specialized, high-stakes motor tasks like professional racing. Iranmanesh’s core contribution lies in designing shared autonomy frameworks that balance human control with algorithmic guidance, enabling personalized, real-time instruction where expert trainers are scarce. By modeling the teacher-learner dynamic as a proximal interaction, she has shown how AI can adaptively scaffold skill acquisition—reducing error rates while preserving the learner’s agency. Her research directly addresses the bottleneck of limited expert availability in fields from surgery to elite sports, offering scalable solutions for precision training. Beyond her technical innovations, Iranmanesh’s work is notable for its human-centered design, ensuring that AI assistance remains transparent and empowering rather than intrusive. With a growing citation footprint and a clear trajectory toward real-world deployment, she is shaping the future of how humans and machines collaborate to master complex physical skills.
Research Focus
Key Achievements
Top Papers
- 1Shared Autonomy for Proximal Teaching3 citations · 2025