Surjeet Singh
Papers
1
Total Citations
19
H-Index
1
About
Surjeet Singh is a researcher at the forefront of humanoid robotics and artificial intelligence, with a primary focus on integrating deep learning into robotic locomotion and planning. His most notable contribution is the development of a footstep planning system for humanoid robots using Generative Adversarial Networks (GANs), a novel approach that leverages deep learning to generate stable, adaptive gaits in real-time. This work, published in 2022, has already garnered 19 citations, signaling its impact on the robotics community. Singh’s research addresses a critical challenge in humanoid robotics—enabling robots to navigate complex, unstructured environments autonomously—by combining ROS (Robot Operating System) with GAN-based algorithms. His approach not only improves the efficiency of footstep planning but also enhances the robot’s ability to generalize across different terrains. Singh’s work is particularly notable for its practical implementation in ROS, making it accessible for further experimentation and deployment. As a rising voice in the intersection of AI and robotics, his contributions are paving the way for more intelligent, adaptable humanoid systems, with potential applications in search-and-rescue, manufacturing, and assistive technologies.
Research Focus
Key Achievements
Top Papers
- 1