EgoSteer: A Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos
EgoSteer is a full-stack system that enables steerable dexterous manipulation by pre-training a VLA model on 9.6K hours of egocentric human videos and post-training on robots. It achieves robust execution of free-form instructions across 40+ tasks, with failure recovery and few-shot adaptation to long-horizon tasks like box folding at 75%+ success.
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[Submitted on 21 Jun 2026]
Title:EgoSteer: A Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos
View a PDF of the paper titled EgoSteer: A Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos, by Yifan Zhong and 15 other authors
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Abstract:Steerability is a defining capability of generalist robot policies, yet remains largely absent in dexterous-hand systems for lack of large-scale, language-aligned, and action-accurate demonstration data. To address this bottleneck, we present a full-stack system that scales dexterous VLA pre-training from egocentric human videos and enables data-efficient real-robot post-training. It integrates EgoSmith, a data pipeline that curates in-the-wild egocentric videos into 9.6K hours of high-quality pre-training data with 9x higher throughput and better accuracy than prior SOTA; a unified robot stack for teleoperation and human-in-the-loop correction; and EgoSteer, a world-model-enhanced VLA trained on optimized infrastructure. Human-data pre-training equips EgoSteer with language-guided manipulation priors, which are grounded through robot post-training and improved by DAgger refinement. Empirically, EgoSteer robustly executes free-form instructions across 40+ diverse tasks, demonstrating failure recovery, dexterity, and generalization. The pre-trained model also few-shot adapts to complex long-horizon tasks, including box folding, on two embodiments with 75+% success. We open-source the system, data, and model at this https URL.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.09701 [cs.RO]
(or arXiv:2607.09701v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.09701
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yifan Zhong [view email] [v1] Sun, 21 Jun 2026 16:16:02 UTC (9,229 KB)
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