Papers

14

Total Citations

188

H-Index

7

About

Karthik Desingh is a robotics researcher whose work sits at the intersection of perception, manipulation, and human-robot interaction. His research addresses some of the most persistent challenges in autonomous robotics: enabling robots to understand and act within complex, cluttered real-world environments. Desingh has made notable contributions to probabilistic scene estimation, developing axiomatic and particle filtering approaches that allow robots to reason about occluded and physically interacting objects during goal-directed manipulation — work that has collectively garnered over 57 citations. His semantic mapping framework, CT-Map, advances simultaneous object detection and 6-DoF pose localization, reflecting his commitment to building robots with rich environmental awareness. Desingh has also pushed the boundaries of articulated object manipulation through efficient nonparametric belief propagation, and explored spatial reasoning for sequential tasks via object-centric neural representations. More recently, his research has embraced natural language understanding, with contributions to 3D visual grounding and end-user directed manipulation learning. Spanning probabilistic inference, deep learning, and human-in-the-loop systems, his body of work charts a coherent path toward robots that are both perceptually capable and genuinely useful to human users.

Research Focus

Key Achievements

7
H-Index
14
Papers
188
Total Citations
13
Avg Citations/Paper
🏆 Most Cited Paper
Goal-directed robot manipulation through axiomatic scene estimation
35 citations · 2017
📈 Most Prolific Year: 2018 (3 Papers)
🤝 Key Collaborators: 28
🏛 Institutions: University of Michigan–Ann Arbor, Brown University, University of Minnesota, University of Minnesota System

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago