Tindyo Prasetyo

Papers

Total Citations

H-Index

About

Tindyo Prasetyo is a researcher at the intersection of robotics, autonomous systems, and intelligent control. His work addresses two critical challenges: bridging the synthetic-to-real gap in autonomous driving datasets and advancing rehabilitation robotics. In his highly cited 2022 paper, "Synthetic to Real Gap Estimation of Autonomous Driving Datasets using Feature Embedding," Prasetyo tackles the fundamental problem of training robust deep learning models for tasks like visual odometry and object detection. By developing methods to estimate and mitigate discrepancies between simulated and real-world data, his work helps enable more reliable autonomous driving systems—a contribution that has already garnered 8 citations shortly after publication. Simultaneously, Prasetyo explores human-robot interaction through his simulation study on Active Disturbance Rejection Control (ADRC) for upper limb exoskeletons. This work addresses the pressing need for effective rehabilitation technologies following spinal cord injuries and accidents, proposing robust control strategies to assist muscle recovery. By spanning both autonomous perception and assistive robotics, Prasetyo demonstrates a versatile research portfolio with tangible impact on both autonomous mobility and human health, making him a promising voice in modern robotics and control engineering.