8 citations · 13 across the 9 of their papers we have counts for
12 papers
Interaction Latents as Actions for Contact-Rich Manipulation
Yuhang Zheng, Yujie Zang, Yuchen Liu +9
Contact-rich manipulation requires robots to coordinate not only where to move, but also how contact and force should evolve throughout an interaction. Yet most robot policies repr…
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…
WALA Learning Executable Latent Actions from Action-Labeled Demonstrations and Action-Free Videos
Jiahao Liu, Zhongpu Xia, Yupeng Zheng +14
Human videos provide rich information about object manipulation at a scale difficult to reproduce with robots, but often lack action annotations that can directly supervise robot p…
Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives
Junli Wang, Zhihua Hua, Xueyi Liu +7
Existing imitation learning methods for end-to-end autonomous driving predominantly learn from successful demonstrations by minimizing geometric deviations from expert trajectories…
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…
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…
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…
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.…
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…
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…
SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems
Xinghan Liu, Guoshun Nan, Qimei Cui +6
Recently proliferated semantic communications (SC) aim at effectively transmitting the semantics conveyed by the source and accurately interpreting the meaning at the destination.…