Angelina Wang

Papers

Total Citations

109

H-Index

About

Angelina Wang is a robotics and machine learning researcher whose work centers on enabling robots to interact intelligently with complex, real-world environments. Her most recognized contribution, "Learning Robotic Manipulation through Visual Planning and Acting" (2019), addresses a fundamental challenge in robotics: how machines can plan and execute manipulation tasks when objects resist straightforward analytical modeling — as is commonly the case in domestic and industrial settings where deformable or irregular materials are involved. By leveraging visual planning frameworks, her research empowers robots to reason about object interactions through learned representations rather than hand-crafted models, significantly broadening the scope of tasks robots can autonomously perform. This work has garnered over 100 citations across its publications, reflecting its meaningful influence within the robotics and artificial intelligence communities. Her contributions are particularly valuable for researchers tackling real-world deployment challenges, where the gap between controlled laboratory conditions and messy practical environments remains a critical obstacle. Wang's research sits at the compelling intersection of computer vision, planning, and robotic learning — areas increasingly vital as the field pushes toward more adaptable, general-purpose robotic systems capable of operating alongside humans in everyday settings.