From the 1 of 9 linked papers with an AI index.
9 papers
GraspGraphNet: Graph-Structured Multi-Embodiment Dexterous Grasp Generation
Yeonseo Lee, Taeyeop Lee, Hyosup Shin +2
The paper presents GraspGraphNet, a graph‑based framework that encodes robot hand kinematics from URDF files and directly generates executable palm poses and joint angles for dexte…
GeoNVS: Geometry Grounded Video Diffusion for Novel View Synthesis
Minjun Kang, Inkyu Shin, Taeyeop Lee +3
Novel view synthesis requires strong 3D geometric consistency and the ability to generate visually coherent images across diverse viewpoints. While recent camera-controlled video d…
Event6D: Event-based Novel Object 6D Pose Tracking
Jae-Young Kang, Hoonhee Cho, Taeyeop Lee +4
Event cameras provide microsecond latency, making them suitable for 6D object pose tracking in fast, dynamic scenes where conventional RGB and depth pipelines suffer from motion bl…
XGrasp: Gripper-Aware Grasp Detection with Multi-Gripper Data Generation
Yeonseo Lee, Jungwook Mun, Hyosup Shin +4
Real-world robotic systems frequently require diverse end-effectors for different tasks, however most existing grasp detection methods are optimized for a single gripper type, dema…
DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulation
Taeyeop Lee, Gyuree Kang, Bowen Wen +5
Despite the prevalence of transparent object interactions in human everyday life, transparent robotic manipulation research remains limited to short-horizon tasks and basic graspin…
Drag4D: Align Your Motion with Text-Driven 3D Scene Generation
Minjun Kang, Inkyu Shin, Taeyeop Lee +2
We introduce Drag4D, an interactive framework that integrates object motion control within text-driven 3D scene generation. This framework enables users to define 3D trajectories f…