Jinda Cui

Lehigh University, Rensselaer Polytechnic Institute, Honda (United States)

Papers

Total Citations

119

H-Index

About

Jinda Cui is a robotics researcher advancing the frontier of intelligent manipulation and autonomous assembly. Her work centers on three key areas: next-generation robot manipulation, large-scale structure assembly, and multi-sensor perception for geometric modeling. Her most influential contribution, "Toward next-generation learned robot manipulation" (2021, 104 citations), addresses the fundamental challenge of robots operating in dynamic human environments, where object properties and robot characteristics change over time—a critical step toward truly adaptive automation. Cui also pioneered a multi-sensor next-best-view framework (2019) that enables robots to construct geometric models tailored to diverse tasks, overcoming the limitations of single-goal reconstruction systems. Her work on operator-guided semi-autonomous assembly (2018) demonstrates practical applications for industrial robots in constructing large segmented structures. Most recently, her 2024 paper "HyperTaxel" introduces a contrastive learning approach to enhance low-resolution tactile signals, pushing toward human-like dexterity. With a research portfolio spanning perception, manipulation, and tactile sensing, Cui is shaping the future of robots that can perceive, adapt, and assemble in unstructured environments.