6 citations · 6 across the 9 of their papers we have counts for
10 papers
VeriVLA: Adaptive Test-Time Verification for Flow-Based VLAs with Latent World Models
Xueli Sun, Yuxiang Lu, Yantong Liu +4
Flow-based Vision-Language-Action (VLA) policies are inherently stochastic: different noise seeds can produce action chunks of varying quality from the same observation. At test ti…
StreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models
Zhe Liu, Jinghua Hou, Yuxiang Lu +7
Vision-Language-Action (VLA) models have demonstrated effectiveness in robot manipulation, yet state-of-the-art models such as pi0.5 operate under a single-frame paradigm, limiting…
FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning
Xirui Li, Zhe Liu, Xiaoqing Ye +4
Multimodal driving planning faces a long-standing tension between two paradigms: scoring-based methods benefit from dense reward supervision but are confined to a fixed action voca…
Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation
Jingyu Li, Zhe Liu, Dongnan Hu +10
World action models~(WAMs) have shown great promise for autonomous driving and urban navigation. Built upon Vision-Language-Action models or video generation models, existing appro…
FASTER: Rethinking Real-Time Flow VLAs
Yuxiang Lu, Zhe Liu, Xianzhe Fan +5
Real-time execution is crucial for deploying Vision-Language-Action (VLA) models in the physical world. Existing asynchronous inference methods primarily optimize trajectory smooth…
UniLION: Towards Unified Autonomous Driving Model with Linear Group RNNs
Zhe Liu, Jinghua Hou, Xiaoqing Ye +3
Although transformers have demonstrated remarkable capabilities across various domains, their quadratic attention mechanisms introduce significant computational overhead when proce…
HybridTM: Combining Transformer and Mamba for 3D Semantic Segmentation
Xinyu Wang, Jinghua Hou, Zhe Liu +1
Transformer-based methods have demonstrated remarkable capabilities in 3D semantic segmentation through their powerful attention mechanisms, but the quadratic complexity limits the…
LION: Linear Group RNN for 3D Object Detection in Point Clouds
Zhe Liu, Jinghua Hou, Xinyu Wang +4
The benefit of transformers in large-scale 3D point cloud perception tasks, such as 3D object detection, is limited by their quadratic computation cost when modeling long-range rel…
OPEN: Object-wise Position Embedding for Multi-view 3D Object Detection
Jinghua Hou, Tong Wang, Xiaoqing Ye +6
Accurate depth information is crucial for enhancing the performance of multi-view 3D object detection. Despite the success of some existing multi-view 3D detectors utilizing pixel-…
SEED: A Simple and Effective 3D DETR in Point Clouds
Zhe Liu, Jinghua Hou, Xiaoqing Ye +3
Recently, detection transformers (DETRs) have gradually taken a dominant position in 2D detection thanks to their elegant framework. However, DETR-based detectors for 3D point clou…