Papers
4
Total Citations
20
H-Index
3
About
Daniel Bargmann is a robotics researcher whose work focuses on the intersection of flexible automation, force-controlled assembly, and intuitive robot programming. His primary research areas include visual servoing, deep reinforcement learning (DRL) for industrial tasks, and Programming-by-Demonstration (PbD) for force-based assembly. Bargmann’s major contributions center on making industrial robots more adaptable and easier to program, particularly for high-precision tasks like gear assembly and wire harness connector installation. His most-cited paper (2023, 8 citations) introduces a vision-guided two-stage approach combining YOLO for coarse localization and DRL for insertion, enabling flexible gear assembly with force feedback. Another influential work (2021, 7 citations) addresses the practical deployment of PbD in industrial settings by leveraging external force-torque sensors to overcome safety and usability barriers. More recently (2024, 3 citations), he has advanced the maintainability of robot programs by extracting composable force- and position-based skills from learning-from-demonstration models. Bargmann’s research directly tackles the longstanding challenge of integrating force-control schemes into real-world assembly, making robotic programming more accessible and reducing the need for deep expertise. His work is highly relevant for students and researchers interested in practical, skill-based robotic automation.
Research Focus
Key Achievements
Top Papers
- 1Flexible Gear Assembly with Visual Servoing and Force Feedback8 citations · 2023
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