Chinmay Shinde
Papers
1
Total Citations
3
H-Index
1
About
Chinmay Shinde is a robotics researcher whose work centers on the intersection of deep reinforcement learning, computer vision, and autonomous aerial systems. His primary research focus is developing intelligent control frameworks that enable drones and moving platforms to perceive and interact with dynamic environments in real time. Shinde’s most notable contribution is the introduction of a Deep Reinforcement Learning-based Image-Based Visual Servoing (DRL-IBVS) controller, which uses monocular images and target bounding box errors to generate linear-velocity commands for tracking objects from a moving platform. This work, published in 2019, demonstrates a novel fusion of deep learning and reinforcement learning to solve the challenging problem of dynamic object detection and tracking without relying on expensive sensor suites. While his citation count is still growing, his research has been recognized for its practical implications in autonomous navigation and surveillance. Shinde’s approach stands out for its potential to reduce computational overhead while maintaining robust tracking performance, making it a valuable contribution to the field of aerial robotics and embodied AI.
Research Focus
Key Achievements
Top Papers
- 1