9 papers
DrivingDepth: Sparse-Prompted Pixel-wise Scale Correction for Driving Depth Estimation
Chi Huang, Wenhao Zhang, Hang Yin +5
Dense depth estimation for autonomous driving faces a geometry-scale conflict: depth foundation models deliver pixel-aligned dense visual geometry without reliable metric scale, wh…
Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation
Jie Zhang, Xiaoyue Chen, Anzhe Chen +36
We introduce Qwen-RobotWorld, a language-conditioned video world model for embodied intelligence. With natural language as a unified action interface, it predicts physically ground…
Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments
Qiuyue Wang, Mingsheng Li, Jian Guan +37
Embodied intelligence is often studied through specialized models for individual tasks such as manipulation or navigation, resulting in fragmented capabilities and limited generali…
F2F-AP: Flow-to-Future Asynchronous Policy for Real-time Dynamic Manipulation
Haoyu Wei, Xiuwei Xu, Ziyang Cheng +5
Asynchronous inference has emerged as a prevalent paradigm in robotic manipulation, achieving significant progress in ensuring trajectory smoothness and efficiency. However, a syst…
CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection
Ziyang Cheng, Haoyu Wei, Hang Yin +4
While decoupled control schemes for legged mobile manipulators have shown robustness, learning holistic whole-body control policies for tracking global end-effector poses remains f…
GC-VLN: Instruction as Graph Constraints for Training-free Vision-and-Language Navigation
Hang Yin, Haoyu Wei, Xiuwei Xu +3
In this paper, we propose a training-free framework for vision-and-language navigation (VLN). Existing zero-shot VLN methods are mainly designed for discrete environments or involv…