Research Engineer in Robotics · Shanghai AI Laboratory
My research interests include reinforcement learning, embodied agents, and robotic manipulation.
I received my M.Sc. in Control Science and Engineering from Shanghai Jiao Tong University in 2026, where I was advised by Prof. Jun-Guo Lu. My previous work includes humanoid imitation and bipedal locomotion.
I am looking for PhD opportunities in robotics and embodied AI.
InfCoiL: Coordinated Planner-Controller Learning for Closed-Loop Physics-Based Human-Object Interaction
A flow-matching planner and a multi-morphology controller learn together through closed-loop reinforcement learning for physical human-object interaction.
Exploratory, Communicative, and Deployable: Vision-Driven Embodied Agents for Open-World Mobile Manipulation
Vision-driven mobile manipulation through active exploration, visual grounding, and interactive intent disambiguation, with a shared interface for simulation and real robots.
How Far Does a Frontier Agent Get as an Embodied Policy?
Evaluating a frontier agent as an embodied policy through single-shot in-context learning, direct robot control, and analysis of manipulation precision, recovery, and task composition.
AnyBipe: An Automated End-to-End Framework for Training and Deploying Bipedal Robots Powered by Large Language Models
Language-guided reward design, reinforcement learning, and sim-to-real evaluation in one end-to-end pipeline for bipedal robots.
I also contributed to the InternW0 and InternW0-Δ technical reports.
PICA-WAM is on arXiv: learning visual perturbation invariance for world action models.
Collaborative technical reports InternW0 and InternW0-Δ are available.
InfCoiL accepted to NeurIPS 2026 — coordinated planning and control for physical human-object interaction.
REAL accepted to ECCV 2026. Explore the paper, benchmark, and open-source code.
GBC released with an end-to-end humanoid imitation pipeline and documentation.
