1 citations · 1 across the 4 of their papers we have counts for
6 papers
UniTrackPLA: Unified Panorama-Language-Action Model for Instruction-Guided Navigation and Dynamic Person Tracking
Pengfei Qi, Haoran Lin, Sizhuang Chen +7
General-purpose embodied robots should support both navigation toward language-specified destinations and dynamic person tracking under arbitrary initial target azimuths. However,…
PanoFuse: Panorama-Enhanced Vision-Language-Action Learning with Decoupled Semantic-Geometric Routing
Peng Xu, Haoran Lin, Wanjun Jia +4
Vision-Language-Action (VLA) policies have shown promising performance in language-conditioned robotic manipulation. However, most existing VLA systems rely on conventional perspec…
TONAV: Task-Oriented Navigation and Action-Velocity Chunk Learning for Articulated Object Quadrupedal Mobile Manipulation
Haoran Lin, Mingyu Yang, Pengfei Qi +6
Quadruped mobile manipulation requires two tightly coupled capabilities: reaching manipulation-ready configurations and maintaining stable contact throughout articulated-object int…
DexPIE: Stable Dexterous Policy Improvement from Real-World Experience
Ruizhe Liao, Wenrui Chen, Liangji Zeng +4
Dexterous manipulation presents substantial challenges for imitation learning due to its high-dimensional action space and complex contact-rich dynamics. Policies trained purely fr…
UniFucGrasp: Human-Hand-Inspired Unified Functional Grasp Annotation Strategy and Dataset for Diverse Dexterous Hands
Haoran Lin, Wenrui Chen, Xianchi Chen +7
Dexterous grasp datasets are vital for embodied intelligence, but mostly emphasize grasp stability, ignoring functional grasps needed for tasks like opening bottle caps or holding…
Learning Granularity-Aware Affordances from Human-Object Interaction for Tool-Based Functional Dexterous Grasping
Fan Yang, Wenrui Chen, Kailun Yang +5
To enable robots to use tools, the initial step is teaching robots to employ dexterous gestures for touching specific areas precisely where tasks are performed. Affordance features…