Xinglin Chen

National University of Defense Technology

Papers

Total Citations

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About

Xinglin Chen is a rising researcher in robotics, focusing on the intersection of multi-robot coordination, human-robot interaction, and adaptive control. Their work primarily addresses the challenge of making Behavior Trees (BTs)—a popular control architecture—both plannable and intuitive for complex, real-world tasks. Chen made a significant contribution by developing MRBTP, a framework for efficient multi-robot behavior tree planning and collaboration, which tackles the critical challenge of extending BTs from single-robot to multi-robot systems. Additionally, they pioneered a method to integrate intent understanding with optimal behavior planning, enabling robots to generate BTs directly from human instructions, thereby enhancing adaptability and reliability in domestic and industrial settings. Chen has also explored the co-adaptation of robot morphology and control, evolving "physical instincts" to improve task performance. With their most-cited works accumulating citations from 2023 to 2025, Chen’s research is gaining traction for its practical approach to bridging high-level human commands with low-level robot execution, marking them as a promising contributor to the future of autonomous and collaborative robotics.