Wenjing Yang

National University of Defense Technology

Papers

Total Citations

H-Index

About

Wenjing Yang is a rising researcher in embodied intelligence and multi-robot systems, whose work bridges the critical gap between robot design and autonomous collaboration. Her research centers on three key areas: multi-robot task planning, behavior tree optimization, and contact-aware robot morphology design. In her most cited work, "MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration" (2025, 3 citations), Yang tackles the formidable challenge of extending Behavior Tree (BT) control architectures—traditionally effective for single robots—to multi-robot systems, proposing a novel planning algorithm that enables efficient coordination among robot teams. Her earlier contribution, "Task2Morph: Differentiable Task-Inspired Framework for Contact-Aware Robot Design" (2023, 2 citations), addresses the fundamental problem of embodied intelligence by introducing a differentiable optimization framework that jointly optimizes robot morphology and controllers for specific tasks, moving beyond traditional search-based methods. While her citation counts reflect an early-career researcher, Yang's work is notable for its technical ambition in unifying robot design and control, and her innovative differentiable approach to morphology optimization represents a significant step toward truly task-adaptive robots. Her research promises to advance both the theoretical foundations and practical capabilities of autonomous robotic systems.