ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining
ACE-Ego-0 is a unified Vision-Language-Action (VLA) pretraining framework that integrates egocentric human videos with robot data by converting human videos into pseudo-action trajectories and using a reliability-aware training objective, boosting model performance on benchmarks and real-world tasks.
[2606.17200] ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining
[Submitted on 15 Jun 2026]
Title:ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining
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Abstract:Vision-Language-Action (VLA) models benefit from large-scale and diverse embodied data, yet scaling robot trajectory collection is costly and labor-intensive. Recent advances show that large-scale egocentric human videos provide complementary real-world supervision in pretraining. However, joint training on human and robot data remains challenging due to divergences in action spaces, embodiment structures, temporal dynamics, and supervision quality. We introduce ACE-EGO-0, a unified VLA pretraining framework jointly leveraging heterogeneous data sources. To extract large-scale pretraining supervision from egocentric human videos, we build a scalable egocentric video-to-action pipeline that converts raw human videos into robot-format pseudo-action trajectories. To make these labels comparable with robot demonstrations, ACE-EGO-0 uses a unified action representation based on camera-space actions, morphology conditioning, and time-aligned action chunking. To robustly leverage noisy pseudo-action supervision from egocentric human videos, we formulate a reliability-aware training objective with a human auxiliary loss that concentrates supervision on reliable signals. We instantiate ACE-EGO-0 on 4.53K hours of robot and simulation data, together with 1.48K hours of pseudo-action-labeled egocentric human data. Experiments show that incorporating large-scale human supervision under reliability-aware weighting consistently improves both unified joint pretraining and supervised fine-tuning. ACE-EGO-0 achieves state-of-the-art performance on RoboCasa GR1 TableTop and RoboTwin 2.0, while demonstrating strong transfer to real-world bimanual manipulation.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.17200 [cs.RO]
(or arXiv:2606.17200v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.17200
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hao Li [view email] [v1] Mon, 15 Jun 2026 18:40:18 UTC (5,934 KB)
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