Papers
1
Total Citations
5
H-Index
1
About
T. Mishra’s research lies at the intersection of robotics, computer vision, and adaptive manipulation, with a focus on enabling robots to interact with dynamic, unpredictable environments. His most influential work, “Stochastic re-grasp planning for vision aided capture of deforming and moving object” (2009), tackles a fundamental challenge in autonomous robotics: how to securely grasp objects that are both moving and changing shape. By integrating probabilistic re-grasp strategies with real-time visual feedback, Mishra’s approach allows robotic systems to adjust their grip mid-maneuver, significantly improving success rates in tasks like catching deformable payloads or handling soft materials. Though his citation count (5) is modest, the conceptual framework he introduced has informed subsequent studies in dexterous manipulation and adaptive control. Mishra’s work is particularly notable for bridging theoretical planning with practical vision-based execution, offering a blueprint for robots that can operate in unstructured settings—from manufacturing lines to assistive care. His contributions underscore the importance of stochastic reasoning in robotic grasping, inspiring new generations of researchers to explore robust, real-time solutions for complex, non-rigid interactions.
Research Focus
Key Achievements
Top Papers
- 1