Papers
6
Total Citations
55
H-Index
4
About
Keshav Iyengar is a leading researcher in the field of medical robotics, with a primary focus on the control and application of Concentric Tube Robots (CTRs) for minimally invasive surgery. His work addresses one of the most challenging problems in continuum robotics: achieving precise, autonomous control of these highly flexible, multi-tube systems. Iyengar’s major contributions lie in pioneering the use of deep reinforcement learning (DRL) for CTR control, developing novel training strategies such as goal-based curricula and Sim2Real transfer techniques to bridge the gap between simulation and physical hardware. His most cited work, "Investigating exploration for deep reinforcement learning of concentric tube robot control" (2020, 24 citations), established foundational methods for this approach. He has also contributed to the clinical translation of robotic systems, notably co-authoring a preclinical validation study for a handheld robot designed for endoscopic endonasal skull base surgery. With a growing body of work accumulating over 50 citations, Iyengar’s research is at the forefront of making autonomous, dexterous robotic tools a reality for delicate surgical interventions.
Research Focus
Key Achievements
Top Papers
- 1
- 2Sim2Real Transfer of Reinforcement Learning for Concentric Tube Robots9 citations · 2023
- 3
- 4Deep Reinforcement Learning for Concentric Tube Robot Path Following6 citations · 2023
- 5
- 6Robot-Assisted Optical Ultrasound Scanning3 citations · 2021