Researchers have introduced HandEdit, a large-scale dataset and benchmark designed to bridge the gap between human hand data and robotic embodiments for Embodied AI. The dataset contains over 200 million editing instances across 26 different URDF configurations, enabling the transformation of human hands into robotic ones. HandEdit aims to facilitate the development of embodiment-aware image-editing models and advance scalable dexterous robotic learning by leveraging abundant human video data. AI
IMPACT Enables more scalable learning for dexterous robotic manipulation by bridging the gap between human and robot data.
RANK_REASON The cluster describes the release of a new dataset and benchmark for a specific research area in AI and robotics.
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- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Embodied Ai
- Gotit.pub
- HandEdit
- Hugging Face
- ScienceCast
- URDFs
- vision-language model
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