Elias Hoerner
Papers
3
Total Citations
18
H-Index
3
About
Elias Hoerner is a researcher at the intersection of haptic sensing, robotic manipulation, and surgical robotics. His work focuses on endowing robots with a sense of touch, aiming to bridge the gap between human tactile perception and machine capabilities. Hoerner’s major contributions include the development of a fingertip 6-axis force/torque sensor for texture recognition in robotic manipulation—a foundational paper with 10 citations that explores how robots can identify surface properties during grasping. He has also advanced surgical robotics by comparing human haptic perception with robotic force/torque sensing in simulated palpation tasks (5 citations), addressing the critical challenge of detecting hard inclusions in soft tissue during minimally invasive surgery. Most recently, Hoerner tackled the classic industrial problem of peg-in-hole insertion with tight clearances, employing a force-based deep Q-learning approach (3 citations) to enable robots to learn complex contact-rich assembly skills through reinforcement learning. His work is notable for its direct application to both humanoid robotics and industrial automation, demonstrating how force feedback can enhance dexterity and precision in tasks ranging from texture recognition to surgical palpation.
Research Focus
Key Achievements
Top Papers
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