A Virtual Controller Framework Across Dataset and Teleoperation
Sponsored by DexRobot
We present a generic policy framework for anthropomorphic dexterous hand manipulation that bridges the gap between human demonstrations and robotic execution. Our framework learns robust, goal-directed manipulation behaviors that generalize across diverse tasks, while preserving human-like grasp and motion structure from limited demonstrations.
Whole-hand force closure demonstration
Full-wrap stabilization technique
Axis-aligned manipulation for tools
Note: Replace YouTube embed codes above with your actual video IDs, or use local video files:
<video controls width="100%">
<source src="videos/demo1.mp4" type="video/mp4">
</video>
ADD-AUC Score: 0.85
ADD-AUC Score: 0.82
We evaluated our model using the Area Under the Curve of Average Distance (ADD-AUC) metric across multiple YCB objects.
High-resolution PDF (3.4 MB)
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Source code and data
@inproceedings{iros2025workshop,
title={Generalized Retargeting for Dexterous Manipulation},
author={Anonymous Submission},
booktitle={IROS 2025 Workshop on Dexterous Manipulation},
year={2025},
location={Hangzhou, China}
}
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