Papers
3
Total Citations
17
H-Index
2
About
Ameya Salvi is a robotics researcher whose work lies at the intersection of control systems, machine learning, and autonomous mobility. His primary research areas include reinforcement learning for robotic manipulation, deep learning for trajectory tracking, and state estimation for mobile robots. In his most cited work, Salvi developed a reinforcement learning control framework for reconfigurable cable-driven parallel manipulators (CDPRs), addressing the significant challenge of controlling these highly nonlinear, underdetermined systems with elastic cables—a contribution that has garnered 8 citations. He has also advanced the field of vision-based control by virtually evaluating deep learning techniques for trajectory tracking, demonstrating how AI-enhanced systems can outperform traditional controllers. More recently, Salvi has tackled the complex problem of wheel-ground interaction in skid-steered mobile robots, proposing an interactive multiple model estimation method for online identification of skidding modes, which is critical for motion prediction and autonomous navigation in outdoor environments. With a growing citation record that reflects the practical relevance of his work, Salvi is establishing himself as a researcher who bridges theoretical control methods with real-world robotic applications.
Research Focus
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