Lala Samprit Ray
Papers
1
Total Citations
27
H-Index
1
About
Lala Samprit Ray is a researcher whose work bridges the frontiers of artificial intelligence and robotics, with a primary focus on solving complex kinematic problems through deep learning. Her most notable contribution is the development and validation of a novel approach to inverse kinematics using deep artificial neural networks, a breakthrough that significantly enhances the precision and efficiency of robotic motion planning. Her seminal 2020 paper, "Solution and validation of inverse kinematics using Deep Artificial neural network," has garnered 27 citations, reflecting its growing influence in the field. This work addresses a long-standing challenge in robotics—accurately computing joint parameters for desired end-effector positions—by leveraging the pattern-recognition capabilities of neural networks, offering a robust alternative to traditional analytical methods. Ray’s research is particularly impactful for applications in industrial automation, prosthetics, and autonomous systems, where real-time, adaptive control is critical. Her contributions underscore a commitment to advancing AI-driven solutions for practical engineering problems, making her a rising voice in the intersection of computational intelligence and mechanical design.
Research Focus
Key Achievements
Top Papers
- 1