5 papers
-VLA: a Hierarchical Robot Foundation Model with World-Model-Guided Test-Time Computation
Xiaowei Cai, Yunuo Cai, Bingao Chen +36
Long-horizon robot manipulation requires a robot to both execute individual skills reliably and sequence them coherently over extended tasks. Most hierarchical vision-language-acti…
Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
Yi Wang, Xinchen Li, Pengwei Xie +13
Generalist robot policies increasingly benefit from large-scale pretraining, but offline data alone is insufficient for robust real-world deployment. Deployed robots encounter dist…
PoseVLA: Universal Pose Pretraining for Generalizable Vision-Language-Action Policies
Haitao Lin, Hanyang Yu, Jingshun Huang +5
Existing Vision-Language-Action (VLA) models often suffer from feature collapse and low training efficiency because they entangle high-level perception with sparse, embodiment-spec…
You Only Estimate Once: Unified, One-stage, Real-Time Category-level Articulated Object 6D Pose Estimation for Robotic Grasping
Jingshun Huang, Haitao Lin, Tianyu Wang +3
This paper addresses the problem of category-level pose estimation for articulated objects in robotic manipulation tasks. Recent works have shown promising results in estimating pa…
CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image
Jingshun Huang, Haitao Lin, Tianyu Wang +3
This paper tackles category-level pose estimation of articulated objects in robotic manipulation tasks and introduces a new benchmark dataset. While recent methods estimate part po…