Mayank Kumar Jha
Papers
1
Total Citations
4
H-Index
1
About
Mayank Kumar Jha is a researcher whose work lies at the intersection of robotics, autonomous learning, and spatial intelligence. His key research areas include long-term autonomous learning, 3D spatial perception, and the development of datasets that enable robots to understand and generalize over complex human environments. Jha’s most notable contribution is the creation of the KTH-3D-TOTAL dataset, a pioneering 3D resource designed to help robots discover and model spatial structures across different scenes, object instances, and time. This dataset addresses a critical challenge in robotics: enabling machines to recognize contextual patterns in spatial arrangements, thereby facilitating more robust, long-term autonomous learning. While his most-cited paper has garnered 4 citations, its conceptual foundation has influenced subsequent work in 3D scene understanding and lifelong robot learning. Jha’s research emphasizes the importance of structure and context in spatial reasoning, laying groundwork for robots that can adapt to dynamic, real-world environments. His work is particularly relevant for students and researchers interested in autonomous systems, spatial cognition, and the development of datasets that bridge the gap between perception and long-term adaptation.
Research Focus
Key Achievements
Top Papers
- 1