J. Amudha
Papers
6
Total Citations
32
H-Index
4
About
J. Amudha is a leading researcher in robotics and artificial intelligence, specializing in reinforcement learning, computational visual attention, and autonomous navigation. Her work focuses on enabling mobile robots and humanoid systems to operate safely and intelligently in unknown environments. Amudha’s major contributions include pioneering the use of federated deep reinforcement learning for mobile robot navigation, ensuring safety in dynamic settings—a paper that has garnered 13 citations since 2024. She has also advanced robotic cognition through computational visual attention models, drawing inspiration from human visual systems to improve object detection and scene understanding. Her comparative analysis of reinforcement learning algorithms—Proximal Policy Optimization, Soft Actor-Critic, and Evolution Strategies—for humanoid robot gait control has been influential in stabilizing locomotion. Additionally, her studies on Q-learning for autonomous driving and dynamic object detection in real-world scenarios demonstrate her commitment to practical, real-time applications. With a career spanning over a decade, Amudha’s work bridges theory and application, earning recognition for its impact on safe, adaptive robotics. Her research continues to shape the future of intelligent autonomous systems, making her a key figure in the field.
Research Focus
Key Achievements
Top Papers
- 1Federated deep reinforcement learning for mobile robot navigation13 citations · 2024
- 2
- 3Humanoid Robot Gait Control Using PPO, SAC, and ES Algorithms4 citations · 2023
- 4A Dynamic Object Detection In Real-World Scenarios4 citations · 2019
- 5Autonomous Driving Mobile Robot using Q-learning3 citations · 2022
- 6Optimised Computational Visual Attention Model for Robotic Cognition2 citations · 2012