IEEE RA-L 2026
ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning
A unified policy combining visual planning with high-frequency force feedback for reactive and temporally coherent robot control.
About Me
I received my bachelor's degree from Shanghai Jiao Tong University in 2026. In September 2026, I will join the School of Artificial Intelligence at Shanghai Jiao Tong University as a Ph.D. student, advised by Prof. Cewu Lu.
I am also affiliated with Shanghai Innovation Institute. I am interested in building robots that can perceive and respond reliably during physical interaction, especially by combining visual information with force feedback.
Research interests: robot manipulation, embodied AI, imitation learning, multimodal learning, and contact-rich interaction.
News
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Jun. 2026
ImplicitRDP was accepted to IEEE Robotics and Automation Letters (RA-L) 2026.
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Feb. 2026
We released the code and models for ImplicitRDP.
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Dec. 2025
We introduced ImplicitRDP, an end-to-end visual-force diffusion policy.
Selected Publications
Education
Sep. 2026 —
Ph.D. Student
School of Artificial Intelligence, Shanghai Jiao Tong University
Advisor: Prof. Cewu Lu · Shanghai Innovation Institute
2022 — 2026
Bachelor of Engineering
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University