8 citations · 14 across the 22 of their papers we have counts for
8 papers · 1 filter
GIFT: Guided Intermediate Feature Training via Action-Oriented Structural Supervision for Robotic Manipulation
Yupeng Zheng, Xiang Li, Songen Gu +11
Vision-language pre-training and predictive world modeling provide robot policies with rich semantic and dynamic visual features, but their native action and visual-prediction obje…
PokeVLA: Empowering Pocket-Sized Vision-Language-Action Model with Comprehensive World Knowledge Guidance
Yupeng Zheng, Xiang Li, Songen Gu +12
Recent advances in Vision-Language-Action (VLA) models have opened new avenues for robot manipulation, yet existing methods exhibit limited efficiency and a lack of high-level know…
WorldRFT: Latent World Model Planning with Reinforcement Fine-Tuning for Autonomous Driving
Pengxuan Yang, Ben Lu, Zhongpu Xia +7
Latent World Models enhance scene representation through temporal self-supervised learning, presenting a perception annotation-free paradigm for end-to-end autonomous driving. Howe…
TakeAD: Preference-based Post-optimization for End-to-end Autonomous Driving with Expert Takeover Data
Deqing Liu, Yinfeng Gao, Deheng Qian +9
Existing end-to-end autonomous driving methods typically rely on imitation learning (IL) but face a key challenge: the misalignment between open-loop training and closed-loop deplo…
Mimir: Hierarchical Goal-Driven Diffusion with Uncertainty Propagation for End-to-End Autonomous Driving
Zebin Xing, Yupeng Zheng, Qichao Zhang +5
End-to-end autonomous driving has emerged as a pivotal direction in the field of autonomous systems. Recent works have demonstrated impressive performance by incorporating high-lev…
UncAD: Towards Safe End-to-end Autonomous Driving via Online Map Uncertainty
Pengxuan Yang, Yupeng Zheng, Qichao Zhang +6
End-to-end autonomous driving aims to produce planning trajectories from raw sensors directly. Currently, most approaches integrate perception, prediction, and planning modules int…
Data Scaling Laws for Imitation Learning-Based End-to-End Autonomous Driving
Yupeng Zheng, Pengxuan Yang, Zhongpu Xia +9
The end-to-end autonomous driving paradigm has recently attracted lots of attention due to its scalability. However, existing methods are constrained by the limited scale of real-w…
PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning
Yupeng Zheng, Zebin Xing, Qichao Zhang +8
Vehicle motion planning is an essential component of autonomous driving technology. Current rule-based vehicle motion planning methods perform satisfactorily in common scenarios bu…