5 papers
Guide, Think, Act: Interactive Embodied Reasoning in Vision-Language-Action Models
Yiran Ling, Qing Lian, Jinghang Li +6
In this paper, we propose GTA-VLA(Guide, Think, Act), an interactive Vision-Language-Action (VLA) framework that enables spatially steerable embodied reasoning by allowing users to…
Distill, Diffuse, Segment: Unsupervised 3D Semantic Segmentation for Autonomous Driving Based on Multi-Level Distillation and Graph Diffusion
Yijing Wang, Ruonan Li, Qilin Wang +2
LiDAR-based semantic segmentation is essential for autonomous-driving perception, yet dense point-wise annotations are costly, and long-tailed outdoor scenes make small safety-crit…
DynVLA: Learning World Dynamics for Action Reasoning in Autonomous Driving
Shuyao Shang, Bing Zhan, Yunfei Yan +9
We propose DynVLA, a driving VLA model that introduces a new CoT paradigm termed Dynamics CoT. DynVLA forecasts compact world dynamics before action generation, enabling more infor…
: An Open Foundation Model Towards Universal Humanoid Loco-Manipulation
Songlin Wei, Hongyi Jing, Boqian Li +12
We introduce (Psi-Zero), an open foundation model to address challenging humanoid loco-manipulation tasks. While existing approaches often attempt to address this fundamenta…
Linking Perception, Confidence and Accuracy in MLLMs
Yuetian Du, Yucheng Wang, Rongyu Zhang +5
Recent advances in Multi-modal Large Language Models (MLLMs) have predominantly focused on enhancing visual perception to improve accuracy. However, a critical question remains une…