8 citations · 14 across the 22 of their papers we have counts for
23 papers
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…
DriveVLA-M0: Failure-Aware Memory Augmentation for Autonomous Driving
Zebin Xing, Yupeng Zheng, Qiang Chen +10
Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for end-to-end autonomous driving by enabling unified reasoning across perception, language, and p…
MemChain: Learning Interpretable Memory Traces for Memory-Augmented LLM Agents
Yiwen Ma, Songjun Tu, Qichao Zhang +3
Memory-augmented LLM agents typically answer queries by retrieving relevant memories and feeding them directly to an answer model. This retrieval-as-evidence paradigm assumes retri…
UCOB: Learning to Utilize and Evolve Agentic Skills via Credit-Aware On-Policy Bidirectional Self-Distillation
Songjun Tu, Chengdong Xu, Qichao Zhang +6
Skill memories can improve agentic reinforcement learning by reusing past experience as textual guidance, but retrieved skills are not oracular: they may help in one state while mi…
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…
DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving
Pengxuan Yang, Yupeng Zheng, Deheng Qian +11
We introduce DreamerAD, the first latent world model framework that enables efficient reinforcement learning for autonomous driving by compressing diffusion sampling from 100 steps…
Forster energy transfer boosts indirect anisotropic interlayer excitons in 2L-MoSe2/perovskite heterostructures
Yingying Chen, Zihao Jiao, Haizhen Wang +1
Interlayer excitons (IXs) in two-dimensional (2D) van der Waals heterostructures have attracted considerable attention due to their unique optical and electronic properties. Owing…
MeanFuser: Fast One-Step Multi-Modal Trajectory Generation and Adaptive Reconstruction via MeanFlow for End-to-End Autonomous Driving
Junli Wang, Yinan Zheng, Xueyi Liu +9
Generative models have shown great potential in trajectory planning. Recent studies demonstrate that anchor-guided generative models are effective in modeling the uncertainty of dr…
Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving
Linbo Wang, Yupeng Zheng, Qiang Chen +14
We introduce Latent-WAM, an efficient end-to-end autonomous driving framework that achieves strong trajectory planning through spatially-aware and dynamics-informed latent world re…
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…
TrajMoE: Scene-Adaptive Trajectory Planning with Mixture of Experts and Reinforcement Learning
Zebin Xing, Pengxuan Yang, Linbo Wang +12
Current autonomous driving systems often favor end-to-end frameworks, which take sensor inputs like images and learn to map them into trajectory space via neural networks. Previous…
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…
Perception-Consistency Multimodal Large Language Models Reasoning via Caption-Regularized Policy Optimization
Songjun Tu, Qichao Zhang, Jingbo Sun +6
While multimodal large language models excel at tasks that integrate visual perception with symbolic reasoning, their performance is often undermined by a critical vulnerability: p…
World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model
Yupeng Zheng, Pengxuan Yang, Zebin Xing +8
End-to-end autonomous driving directly generates planning trajectories from raw sensor data, yet it typically relies on costly perception supervision to extract scene information.…
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Yuqian Fu, Tinghong Chen, Jiajun Chai +7
Large language models (LLMs) have achieved remarkable progress in reasoning tasks, yet the optimal integration of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) remai…
ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-loop Autonomous Driving
Xueyi Liu, Zuodong Zhong, Yuxin Guo +9
Due to the powerful vision-language reasoning and generalization abilities, multimodal large language models (MLLMs) have garnered significant attention in the field of end-to-end…
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…
DeltaDiff: Reality-Driven Diffusion with AnchorResiduals for Faithful SR
Chao Yang, Yong Fan, Qichao Zhang +2
Recently, the transfer application of diffusion models in super-resolu-tion tasks has faced the problem ofdecreased fidelity. Due to the inherent randomsampling characteristics ofd…
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…
MonoOcc: Digging into Monocular Semantic Occupancy Prediction
Yupeng Zheng, Xiang Li, Pengfei Li +6
Monocular Semantic Occupancy Prediction aims to infer the complete 3D geometry and semantic information of scenes from only 2D images. It has garnered significant attention, partic…
DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning
Xiao-Yin Liu, Xiao-Hu Zhou, Mei-Jiang Gui +7
Model-based reinforcement learning (RL), which learns an environment model from the offline dataset and generates more out-of-distribution model data, has become an effective appro…