Sreevathsa Sree Charan
Papers
1
Total Citations
4
H-Index
1
About
Sreevathsa Sree Charan is a robotics researcher whose work focuses on advancing locomotion and control in humanoid robots through reinforcement learning. His most-cited paper, "Humanoid Robot Gait Control Using PPO, SAC, and ES Algorithms" (2023), provides a critical comparative analysis of three major reinforcement learning algorithms—Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Evolution Strategies (ES)—for stabilizing and improving humanoid gait. By systematically evaluating these approaches, Charan has contributed to understanding how different learning paradigms affect robot stability and movement efficiency, a foundational challenge in humanoid robotics. His work directly addresses the core issue of gait control, which is essential for enabling humanoid robots to navigate complex, real-world environments. With 4 citations, this paper has already begun influencing subsequent research in adaptive locomotion. Charan’s research sits at the intersection of robotics and artificial intelligence, offering practical insights for developing more robust, autonomous humanoid systems. His contributions are particularly valuable for students and engineers seeking to implement reinforcement learning-based control strategies in real robotic platforms.
Research Focus
Key Achievements
Top Papers
- 1Humanoid Robot Gait Control Using PPO, SAC, and ES Algorithms4 citations · 2023