Shreshth Tuli
Imperial College London, Indian Institute of Technology Delhi
Papers
5
Total Citations
13
H-Index
2
About
Shreshth Tuli is a robotics researcher whose work focuses on enabling robots to understand and interact with their physical environments more intelligently. His primary research areas include commonsense reasoning for tool use, robot plan synthesis, and 3D object modeling for manipulation. Tuli’s most significant contributions center on developing AI systems that allow robots to generalize knowledge about when and how to use everyday objects as tools—a capability essential for autonomous operation in homes and factories. His papers on TOOLTANGO, ToolNet, and TANGO (cumulatively cited over 9 times) address the challenge of learning sequential tool interactions and composing them to accomplish high-level tasks. More recently, his work on ActNeRF introduces uncertainty-aware active learning to rapidly build complete 3D models of unfamiliar objects through physical interaction, enabling more robust manipulation. Tuli’s research on GoalNet further advances robot instruction following by inferring goal predicates from human demonstrations. Through these contributions, he is helping bridge the gap between robotic perception and commonsense physical reasoning, laying groundwork for more capable and adaptable service robots.
Research Focus
Key Achievements
Top Papers
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