40 citations · 57 across the 23 of their papers we have counts for
10 papers · 1 filter
WorldGuide: Learning Success-Failure Boundaries in Latent World Models for Vision-Language-Action Policies
Lin Liu, Lu Zhang, Ziying Song +6
Latent world models offer a promising way to improve Vision-Language-Action policies by capturing the consequences of actions. However, models trained primarily on expert demonstra…
MomWorld: Momentum-Aware Latent World Model for Long-Horizon Autonomous Driving
Ziying Song, Shengkai Zhang, Lei Yang +6
Long-horizon planning enables autonomous vehicles to anticipate scene evolution and potential risks, supporting safe and stable decisions in complex interactions. However, existing…
Towards robust multimodal 3D object detection via visual foundation models
Ziying Song, Lin Liu, Hongyu Pan +4
Multimodal 3D object detection is fundamental to robust perception in autonomous driving because it integrates complementary information from LiDAR and camera sensors. However, exi…
LIBERO-RECOVER: Beyond Task Success Towards Failure Recovery in Robotic Manipulation Models
Lin Liu, Zhicheng Bao, Lu Zhang +7
Vision-Language-Action (VLA) or World Action (WAM) models have recently demonstrated remarkable performance in robotic manipulation. On LIBERO, SOTA method have achieved nearly 100…
MomADv2: Reliable Temporal Memory for End-to-End Autonomous Driving
Ziying Song, Shengkai Zhang, Lin Liu +8
Long-horizon planning is critical for safe autonomous driving in complex scenarios. Existing methods improve planning continuity with temporal memory, but such memory may become in…
GraphBEV++: Multi-Modal Feature Alignment for Autonomous Driving
Ziying Song, Caiyan Jia, Lin Liu +3
Feature misalignment in BEV perception is a critical yet often overlooked challenge in autonomous driving, especially under calibration uncertainties between LiDAR and camera senso…
GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Driving
Ziying Song, Caiyan Jia, Lin Liu +8
End-to-end autonomous driving has made significant progress by unifying perception, prediction, and planning within a single learning framework, achieving strong performance in sho…
DriveFuture: Future-Aware Latent World Models for Autonomous Driving
Yufeng Hong, Xiaotian Zhou, Yingyan Li +6
Existing latent world models for autonomous driving have opened a promising path toward future-aware driving intelligence. However, they typically treat future latent states as pre…
Xiaomi OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation
Jinghui Lu, Jiayi Guan, Zhijian Huang +47
Chain-of-Thought (CoT) reasoning has become a powerful driver of trajectory prediction in VLA-based autonomous driving, yet its autoregressive nature imposes a latency cost that is…
DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving
Feiyang jia, Lin Liu, Ziying Song +4
End-to-end (E2E) autonomous driving has recently attracted increasing interest in unifying Vision-Language-Action (VLA) with World Models to enhance decision-making and forward-loo…