RoPL


At RoPL, we study how robots can learn, reason, and act intelligently in an open, complex, and dynamic world.

Latest Preprints

Wenjing Tang, Xuanjin Jin, Yuan Liu, Renming Huang, Cewu Lu, Panpan Cai (2026). PO-PDDL: Learning Symbolic POMDPs from Visual Demonstrations for Robot Planning Under Uncertainty. Advances in Neural Information Processing Systems (NeurIPS), 2026.

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Yongxi Huang, Zhuohang Wang, Wenjing Tang, Xinyu He, Cewu Lu, Panpan Cai (2026). I-Perceive: A Foundation Model for Vision-Language Active Perception. Advances in Neural Information Processing Systems (NeurIPS), 2026.

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Renming Huang, Chendong Zeng, Wenjing Tang, Jintian Cai, Cewu Lu, Panpan Cai (2026). Mimic Intent, Not Just Trajectories. arXiv preprint arXiv:2602.08602.

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Xuanjin Jin, Yanxin Dong, Bin Sun, Huan Xu, Zhihui Hao, XianPeng Lang, Panpan Cai (2026). Vec-QMDP: Vectorized POMDP Planning on CPUs for Real-Time Autonomous Driving. arXiv preprint arXiv:2602.08334.

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Zhihong Liu, Yang Li, Renming Huang, Chendong Zeng, Cewu Lu, Panpan Cai (2026). TGPO: Trace-Guided Policy Optimization for Robot Task Planning via Verifiable Subgoal Generation. Advances in Neural Information Processing Systems (NeurIPS), 2026.

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