While learning dexterous manipulation from a single human video offers a promising alternative to costly robot demonstrations, many recent methods predominantly imitate demonstrated motions. Such strict motion matching often limits generalization to initial object poses, goal poses, and grasps not shown in the video. A...
Jusuk Lee, Sunghae Kim, Yeonsoo Park et al.· 0 citations
3D Gaussian Splatting (3DGS) has emerged as an effective scene representation for visual localization, but most existing methods rely on embedding keypoint descriptors into Gaussian primitives, leading to high memory overhead and requiring joint optimization of geometry and features. We propose SparKLoc, a visual local...
Gyeong Chan Kim, Youngseok Jang, Jeong-Dae Heo et al.· IEEE Robotics and Automation...· 0 citations
We present DXPR, a depth-based cross-modal place recognition (CMPR) framework that uses vision foundation models (VFMs) to match monocular camera queries against a LiDAR map without modality-specific encoders. This enables robots and autonomous vehicles to robustly localize using only cameras within pre-built LiDAR map...
Yu-Hang Han, Youngseok Jang, Seungwon Roh et al.· 0 citations
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