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About

Kejia Ren is a roboticist whose research lies at the intersection of manipulation, perception, and motion planning, with a particular focus on enabling robots to operate intelligently in cluttered and uncertain environments. Ren’s major contributions center on rearrangement-based manipulation, where they have developed kinodynamic planning strategies that allow robots to sequentially move multiple objects to reach a target—a critical capability for real-world tasks like warehouse picking or home organization. Their work on “Rearrangement-Based Manipulation via Kinodynamic Planning and Dynamic Planning Horizons” (2022, 11 citations) provides a foundational framework for this complex problem. Ren has also advanced interactive perception through “RISeg: Robot Interactive Object Segmentation via Body Frame-Invariant Features” (2024, 5 citations), which enables robots to segment unseen objects during manipulation, bridging the gap between perception and action. Additionally, their unified pushing framework, “UNO Push” (2024, 3 citations), tackles nonprehensile manipulation under physical uncertainties, while their exploration of collision-inclusive planning and caging-in-time strategies (2025) pushes the boundaries of robust manipulation under limited perception. Ren’s work is notable for its practical, uncertainty-aware approach, making it highly relevant for students and researchers interested in autonomous manipulation in the wild.