DexOAK: Object-Aware Kinematic Retargeting
DexOAK: Object-Aware Kinematic Retargeting
DexOAK is a learning-based framework that generates contact-consistent robot hand references from human motion-capture data. It uses an interaction-graph Laplacian objective to reproduce object-relative interaction geometry, and a feed-forward network conditioned on human keypoints and object point clouds to learn retargeting. Object-aware finetuning refines the network under contact, non-penetration, and self-collision losses, improving performance on tasks like OakInk-v2 and ARCTIC. The model transfers to unseen data and enables dexterous robot data generation from human data.
DexOAK introduces a learning-based framework for generating contact-consistent robot hand references from human motion data, improving dynamic retargeting performance on bimanual tasks with rigid and articulated objects.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
number of tasks
5
View original evidence
mortized network refined with contact losses. Trained on OakInk-v2, it transfers to ARCTIC zero-shot and nears in-domain performance after fine-tuning. On hardware, 46/56 open-loop replays succeed across five tasks, enabling dexterous robot data generation from human data. Visualizations and interactive results are at: https://fspinola.github.io/dexoak/.
Open source S5
- paper: not_reported
- code: not_reported
- dataset: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
trained, the network transfers to unseen data by inference alone, without per-frame optimization. DexOAK references improve dynamic retargeting over the state-of-the-art across bimanual tasks with rigid and articulated objects. On the Inspire hand, success rises from 39.5% to 48.4% on OakInk-v2, and ADD-AUC rises from 62.7% to 77.9% on ARCTIC. Ablations trace the gains to object-aware kinematic retargeting and the a
Open source S4
mortized network refined with contact losses. Trained on OakInk-v2, it transfers to ARCTIC zero-shot and nears in-domain performance after fine-tuning. On hardware, 46/56 open-loop replays succeed across five tasks, enabling dexterous robot data generation from human data. Visualizations and interactive results are at: https://fspinola.github.io/dexoak/.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org