Anuh Pasricha
Papers
1
Total Citations
2
H-Index
1
About
Anuh Pasricha is a researcher at the forefront of integrating machine learning with robotics, specializing in transformer-based models for dynamical systems. His most-cited work, "Transformer-based Learning Models of Dynamical Systems for Robotic State Prediction" (2024), introduces a novel framework that leverages attention mechanisms to enhance the accuracy and efficiency of predicting robotic states in complex, dynamic environments. This contribution addresses a critical challenge in robotics—enabling systems to anticipate and adapt to real-world changes—by moving beyond traditional recurrent architectures. With 2 citations in its early publication stage, this paper signals growing interest in his approach, which has potential applications in autonomous navigation, manipulation, and human-robot interaction. Pasricha’s research bridges deep learning and control theory, offering scalable solutions for robots operating in uncertain conditions. His work is particularly notable for its focus on data-efficient learning, reducing the reliance on extensive real-world training data. As a rising voice in the field, Pasricha’s innovations promise to advance the next generation of intelligent, adaptive robotic systems, making him a researcher to watch for students and professionals exploring the intersection of AI and robotics.
Research Focus
Key Achievements
Top Papers
- 1