Papers
4
Total Citations
90
H-Index
4
About
Taryudi Taryudi is a leading researcher in robotics and computer vision, with a focus on enhancing the precision and autonomy of industrial manipulators. His core contributions lie in stereo vision-based object manipulation, where he has pioneered methods for accurate 3D pose estimation and eye-to-hand calibration. Notably, his 2017 work on using ANFIS (Adaptive Neuro-Fuzzy Inference System) for calibration has garnered 36 citations, demonstrating its impact on improving robotic grasping accuracy. His subsequent studies on binocular vision-based tracking and grabbing (16 citations) and 3D object pose estimation (21 citations) have further advanced the field, enabling robots to interact more reliably with their environments. Beyond technical robotics, Taryudi has explored the human-robot interface, particularly in healthcare. His 2022 qualitative study on nurses’ views toward robotic use during the COVID-19 pandemic in Indonesia (17 citations) highlights his commitment to understanding societal readiness for automation. This work bridges engineering and social science, offering critical insights for integrating robots into human-centered settings. Taryudi’s research not only pushes the boundaries of robotic manipulation but also addresses the practical and ethical challenges of deploying these systems in real-world applications.
Research Focus
Key Achievements
Top Papers
- 1
- 23D object pose estimation using stereo vision for object manipulation system21 citations · 2017
- 3
- 4Eye-to-hand robotic tracking and grabbing based on binocular vision16 citations · 2019