1 citations · 1 across the 15 of their papers we have counts for
18 papers
AVERT-VLN: Abstention-aware Visual Error Recovery and Training for Vision-and-Language Navigation
Minrui Liu, Jingke Wang, Yuehao Huang +4
Deploying vision-and-language navigation (VLN) agents in unseen environments remains challenging because unfamiliar layouts and visual conditions can cause execution to go off trac…
RiverVLN: Phase-Grounded Temporal Vision--Language Navigation for Unmanned Surface Vehicles
Jieling Wu, Yuehao Huang, Jiajun Lv +3
Vision-language navigation (VLN) has largely been developed for indoor and terrestrial robots, where language can often be treated as a static goal and motion is approximated by di…
WNM-3D: A World Navigation Model with 3D Scene Conditioning for Closed-Loop VLN
Yuehao Huang, Yunzi Wu, Xiaotao Zhang +7
Recent vision-language navigation (VLN) systems increasingly adapt pretrained vision-language models (VLMs) into vision-language-action (VLA) policies that map egocentric observati…
TS-Mask VLA: 2D Temporal-Spatial Masking for Vision-Language-Action Model with Effective Bridging
Shengzhuo Yang, Ronghao Yu, Chuanjie Lv +5
Vision-language-action (VLA) models aim to understand natural-language instructions and visual observations, and to generate and execute corresponding actions as embodied agents. R…
SAGE-Nav: Leveraging LLM Planning and Alignment Fusion for Hierarchical Scene Graph-Guided Navigation
Hao Su, Yuehao Huang, Yukai Ma +2
Object-Goal Navigation (ObjNav) requires embodied agents to autonomously locate specified targets using only egocentric visual observations. Existing monolithic methods struggle wi…
DriveStack-VLA: Render-Teacher Alignment for BEV-Based DeepStack Vision-Language-Action Model
Jingke Wang, Zhenru Zhao, Shuangming Lei +8
Vision-Language-Action driving models convert a pretrained Vision-Language Model into a driving policy, allowing them to use world knowledge and follow language guidances. However,…
SparseWorld: Enhancing End-to-End Autonomous Driving via World Models with Sparse Scene Representation
Ruoyu Wang, Jingke Wang, Yukai Ma +5
Recently, world models have made significant progress in enhancing end-to-end driving systems through both future situation forecasting and improved scene understanding. However, e…
Monocular 3D Occupancy Perception for Robots on Sidewalks via Hybrid 2D-3D Learning
Yukai Ma, Joe Lin, Liu Liu +5
Sidewalks in the real world are crowded, cluttered, and less structured than roads, making 3D occupancy prediction a key ingredient for the safe navigation of mobile robots such as…
VEOcc: Voxel-Centric Online Semantic Occupancy Prediction For Embodied Scene Understanding
Ruoyu Wang, Yong Liu, Jiahan Li +3
Crucial for autonomous exploration, online 3D occupancy prediction and mapping incrementally construct dense spatial representations on the fly. Embodied online occupancy predictio…
NEWTON: Agentic Planning for Physically Grounded Video Generation
Yuxiang Feng, Juncheng Wang, Chao Xu +7
Video generation models produce visually compelling results but systematically violate physical commonsense -- on VideoPhy-2, the best model achieves only 32.6% joint accuracy. We…
Vision-Centric 4D Occupancy Forecasting and Planning via Implicit Residual World Models
Jianbiao Mei, Yu Yang, Xuemeng Yang +4
End-to-end autonomous driving systems increasingly rely on vision-centric world models to understand and predict their environment. However, a common ineffectiveness in these model…
3D and 4D World Modeling: A Survey
Lingdong Kong, Yu Yang, Jianbiao Mei +20
World modeling has become a cornerstone in AI research, enabling agents to understand, represent, and predict the dynamic environments they inhabit. While prior work largely emphas…
CogDDN: A Cognitive Demand-Driven Navigation with Decision Optimization and Dual-Process Thinking
Yuehao Huang, Liang Liu, Shuangming Lei +7
Mobile robots are increasingly required to navigate and interact within unknown and unstructured environments to meet human demands. Demand-driven navigation (DDN) enables robots t…
X-Scene: Large-Scale Driving Scene Generation with High Fidelity and Flexible Controllability
Yu Yang, Alan Liang, Jianbiao Mei +3
Diffusion models are advancing autonomous driving by enabling realistic data synthesis, predictive end-to-end planning, and closed-loop simulation, with a primary focus on temporal…
LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process Thinking
Yukai Ma, Tiantian Wei, Naiting Zhong +6
While autonomous driving technology has made remarkable strides, data-driven approaches still struggle with complex scenarios due to their limited reasoning capabilities. Meanwhile…
DreamForge: Motion-Aware Autoregressive Video Generation for Multi-View Driving Scenes
Jianbiao Mei, Tao Hu, Xuemeng Yang +7
Recent advances in diffusion models have improved controllable streetscape generation and supported downstream perception and planning tasks. However, challenges remain in accurate…
DQFormer: Towards Unified LiDAR Panoptic Segmentation with Decoupled Queries
Yu Yang, Jianbiao Mei, Liang Liu +6
LiDAR panoptic segmentation, which jointly performs instance and semantic segmentation for things and stuff classes, plays a fundamental role in LiDAR perception tasks. While most…
Driving in the Occupancy World: Vision-Centric 4D Occupancy Forecasting and Planning via World Models for Autonomous Driving
Yu Yang, Jianbiao Mei, Yukai Ma +5
World models envision potential future states based on various ego actions. They embed extensive knowledge about the driving environment, facilitating safe and scalable autonomous…