Yanjun Chen

Chongqing University of Posts and Telecommunications

Papers

Total Citations

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About

Yanjun Chen is a robotics researcher whose work sits at the intersection of robot learning, manipulation, and control systems. Chen’s most prominent contribution is the VIMA system (2022, 65 citations), a groundbreaking framework that brings prompt-based learning—a paradigm that revolutionized natural language processing—into general robot manipulation. VIMA enables a single robot model to perform a wide range of tasks specified through multimodal prompts, including images, text, and one-shot demonstrations, effectively bridging the gap between high-level language instructions and low-level motor control. This work has become a foundational reference for researchers exploring foundation models in robotics. Chen has also contributed to practical control challenges, proposing an improved Deep Deterministic Policy Gradient (DDPG) algorithm for grasp trajectory planning in vehicle-mounted robotic arms (2024), addressing issues of slow convergence and poor control in dynamic environments. By combining cutting-edge learning paradigms with real-world robotic applications, Chen’s research offers a compelling vision for more flexible, general-purpose robotic systems that can understand and execute complex instructions across diverse tasks.