Papers
3
Total Citations
25
H-Index
3
About
Dr. Prithwijit Guha is a leading researcher in robotics and artificial intelligence, with a primary focus on autonomous navigation, manipulation, and reinforcement learning. His early seminal work on path planning for statically stable biped robots, integrating Probabilistic Roadmaps (PRM) with reinforcement learning (14 citations), laid foundational insights for legged locomotion in complex environments. Dr. Guha has since advanced deep reinforcement learning, notably through his work on recurrent convolutional neural networks (6 citations), which enables policy learning in partially observable settings without explicit task modeling—a critical breakthrough for real-world robotic applications. He further contributed to robotic manipulation with his research on stochastic re-grasp planning for vision-aided capture of deforming and moving objects (5 citations), addressing dynamic and uncertain environments. Dr. Guha’s work bridges theoretical machine learning with practical robotics, earning him recognition for developing algorithms that enhance robot autonomy in unstructured settings. His research continues to inspire students and researchers exploring the intersection of computer vision, control, and learning.
Research Focus
Key Achievements
Top Papers
- 1
- 2Reinforcement Learning via Recurrent Convolutional Neural Networks6 citations · 2016
- 3