Bingjie Tang

Southern California University for Professional Studies, University of Southern California

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About

Bingjie Tang is a robotics researcher focused on advancing contact-rich manipulation and sim-to-real transfer for industrial and cluttered environments. Her major contributions lie in developing learning-based policies that enable robots to perform complex assembly tasks and collaborative pushing-and-grasping with high precision and adaptability. Tang’s work on "IndustReal" (2023, 42 citations) demonstrates a pioneering framework for transferring contact-rich assembly skills from simulation to reality, tackling longstanding challenges in precision and cycle time. Her earlier research on "Learning Collaborative Pushing and Grasping Policies in Dense Clutter" (2021, 32 citations) introduced joint reasoning over pushing and grasping actions, moving beyond isolated operations to enable flexible manipulation in cluttered settings. Most recently, her FORGE framework (2025, 10 citations) addresses pose uncertainty in force-aware manipulation, combining simulation-based learning with force-guided exploration for robust real-world performance. Tang’s work has significant implications for manufacturing and logistics automation, bridging the gap between simulated training and reliable physical execution. Her achievements highlight her as a rising leader in robotic manipulation and sim-to-real transfer.