7 papers
Towards Human-Like Manipulation through RL-Augmented Teleoperation and Mixture-of-Dexterous-Experts VLA
Tutian Tang, Xingyu Ji, Wanli Xing +7
While Vision-Language-Action (VLA) models have demonstrated remarkable success in robotic manipulation, their application has largely been confined to low-degree-of-freedom end-eff…
Stereo-Inertial Poser: Towards Metric-Accurate Shape-Aware Motion Capture Using Sparse IMUs and a Single Stereo Camera
Tutian Tang, Xingyu Ji, Yutong Li +3
Recent advancements in visual-inertial motion capture systems have demonstrated the potential of combining monocular cameras with sparse inertial measurement units (IMUs) as cost-e…
FSGlove: An Inertial-Based Hand Tracking System with Shape-Aware Calibration
Yutong Li, Jieyi Zhang, Wenqiang Xu +2
Accurate hand motion capture (MoCap) is vital for applications in robotics, virtual reality, and biomechanics, yet existing systems face limitations in capturing high-degree-of-fre…
FBI: Learning Dexterous In-hand Manipulation with Dynamic Visuotactile Shortcut Policy
Yijin Chen, Wenqiang Xu, Zhenjun Yu +4
Dexterous in-hand manipulation is a long-standing challenge in robotics due to complex contact dynamics and partial observability. While humans synergize vision and touch for such…
GarmentTracking: Category-Level Garment Pose Tracking
Han Xue, Wenqiang Xu, Jieyi Zhang +5
Garments are important to humans. A visual system that can estimate and track the complete garment pose can be useful for many downstream tasks and real-world applications. In this…
DexTOG: Learning Task-Oriented Dexterous Grasp with Language
Jieyi Zhang, Wenqiang Xu, Zhenjun Yu +3
This study introduces a novel language-guided diffusion-based learning framework, DexTOG, aimed at advancing the field of task-oriented grasping (TOG) with dexterous hands. Unlike…