Nilesh Modak
Papers
1
Total Citations
10
H-Index
1
About
Nilesh Modak’s research sits at the intersection of computer vision and robotics, with a focus on enabling machines to perceive and interact with their environments in real time. His most cited work, “Design framework for general purpose object recognition on a robotic platform” (2017, 10 citations), addresses a critical challenge: balancing the accuracy of deep convolutional neural networks with the computational constraints of robotic systems. Modak proposes a streamlined framework that adapts state-of-the-art object detection methods for real-time deployment, making it feasible for robots to identify and tag objects without sacrificing performance. This contribution is particularly valuable for autonomous navigation and manipulation tasks, where speed and reliability are paramount. Though his citation count is modest, Modak’s work exemplifies the practical engineering needed to bridge advanced computer vision algorithms and physical robotic platforms. His research underscores a commitment to creating robust, general-purpose recognition systems that can operate outside controlled lab settings—a crucial step toward more capable and responsive autonomous agents.
Research Focus
Key Achievements
Top Papers
- 1