Niladri Das
Papers
2
Total Citations
13
H-Index
2
About
Niladri Das is a roboticist whose research centers on the modeling, control, and learning of advanced robotic manipulators. His work addresses the fundamental challenge of achieving precise, reliable motion in complex robotic systems. Das’s most cited paper, "Control of a 4 DoF Barrett WAM robot," develops and experimentally validates a dynamic model for the Barrett Whole Arm Manipulator (WAM), a key contribution for enabling accurate model-based control. This work has garnered 8 citations, establishing a foundation for subsequent research. Expanding beyond classical control, Das explores how robots can learn from human demonstration. In "Learning object manipulation from demonstration through vision for the 7-DOF Barrett WAM," he integrates a Microsoft Kinect 3D sensor with a symbolic encoding technique to teach a 7-DoF WAM manipulator new skills through vision, a departure from traditional trajectory-focused methods. This 5-citation paper highlights his interest in making robot programming more intuitive. Through these contributions, Das has advanced both the theoretical modeling and practical skill acquisition of dexterous robotic arms, bridging the gap between precise control and adaptive learning.
Research Focus
Key Achievements
Top Papers
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