Learning dexterous manipulation benefits from human demonstration datasets that capture diverse and natural hand-object interactions. In particular, contact points and forces provide supervision on where and how strongly to interact, which cannot be fully captured by motion trajectories alone. However, methods for join...
Youbeen Jeon, Uiseong Shin, Hwanchul La et al.· 0 citations
Teleoperation is becoming increasingly important for collecting high-quality demonstrations to teach robots dexterous manipulation skills. For dexterous manipulation, bare-hand tracking provides a practical way to control robotic hands and demonstrate coordinated finger movements without gloves or exoskeletons. However...
Youngchan Shim, Kyutae Lee, JooYun Kim et al.· 0 citations
This work proposes the first end-to-end pipeline that reconstructs simulation-ready assets with accurate articulation from a single static object video input through suggestion based human-in-the-loop process, and achieves precise articulation results on partnet-mobility-v0 dataset and real objects.
Hyesung Lee, Young-Seon Lee, Kyutae Lee et al.· 1 citation
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