Shivam Singh
Papers
1
Total Citations
2
H-Index
1
About
Shivam Singh is a leading researcher in embodied AI and human-robot interaction, with a focus on enabling robots to adapt to novel tasks without extensive retraining. His work centers on integrating Large Language Models (LLMs) with structured knowledge representations, such as knowledge graphs, and human input to create more flexible and intelligent robotic agents. Singh’s major contribution is the development of the AdaptBot framework, which combines LLMs with knowledge graphs and human feedback to decompose complex, generic tasks into specific, executable steps. This approach allows robots to dynamically refine their understanding and actions, bridging the gap between broad world knowledge and precise, context-aware execution. Although his most cited paper, "AdaptBot," is recent (2025) with 2 citations, it represents a significant step toward practical, adaptable embodied agents. Singh’s work is highly relevant for students and researchers interested in the intersection of natural language processing, knowledge representation, and robotics, offering a pathway to more autonomous and helpful AI systems that can learn and perform in real-world, unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1