The Human Scale: NVIDIA’s EgoScale Unlocks High-Dexterity Robotics via 20,000 Hours of Human Video

A four-panel grid of egocentric human videos showing daily activities like ironing a shirt and handling baked goods. Red and blue skeletal overlays track the movement of the human hands. — Scaling with egocentric data: EgoScale pretrains on over 20,000 hours of human video spanning thousands of unique tasks and environments. Precise skeletal hand tracking (shown in red and blue) allows the model to extract and retarget 21 keypoints of human motion into a unified robotic action space.

NVIDIA researchers have introduced EgoScale, a new human-to-robot transfer framework that suggests the path to master-level robot dexterity isn't more robot data, but more human video. By pretraining on a massive 20,854-hour dataset of egocentric human manipulation, the team has uncovered a "predictable scaling law" where human action prediction directly correlates with downstream robotic success.

The project, led by NVIDIA’s GEAR Lab, marks a significant departure from traditional robotics training which often relies on expensive, slow robot teleoperation. Instead, EgoScale treats humans as the most "scalable embodiment" on the planet, using their everyday movements to build a foundational motor prior for machines.

Jim Fan

@DrJimFan

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We trained a humanoid with 22-DoF dexterous hands to assemble model cars, operate syringes, sort poker cards, fold/roll shirts, all learned primarily from 20,000+ hours of egocentric human video with no robot in the loop. Humans are the most scalable embodiment on the planet. We

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5:22 PM · Feb 25, 2026

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The Scaling Law of Dexterity

At the heart of EgoScale is a dataset more than 20 times larger than prior efforts in human-robot policy transfer. Spanning over 9,000 scenes and 6,000 tasks, the data provides a long-tail coverage of real-world manipulation—from assembling boxes to handling delicate electronics.

The researchers discovered a near-perfect log-linear scaling law ( $R^2 = 0.9983$ ) between the volume of human data and the model's validation loss. As data scales, the model’s ability to predict human wrist and hand actions improves monotonically, which in turn leads to a consistent rise in real-robot performance.

"This offline scaling behavior is strongly predictive of real-robot performance," the researchers noted, establishing that large-scale human video is a predictable supervision source for embodied intelligence.

A Simple Three-Stage Recipe

The EgoScale framework bypasses complex transfer algorithms in favor of a straightforward training pipeline:

Pretraining (Human Data): A Vision-Language-Action (VLA) model is trained on the 20,000+ hours of human video. To bridge the embodiment gap, human hand motions are retargeted into a 22-degree-of-freedom (DoF) robotic hand joint space.
Mid-training (Aligned Data): The model is "anchored" to robot sensing using a small, 54-hour dataset of aligned human-robot "play data". This stage is critical for grounding human-derived representations in executable robot control.
Post-training (Task Specific): The policy is fine-tuned on specific downstream tasks.

This method resulted in a 54% improvement in average success rates over baselines trained without human pretraining. The model successfully mastered high-dexterity tasks including card sorting, unscrewing bottle caps, and even the multi-step process of using a syringe to transfer liquids.

Emergent One-Shot Capabilities

Perhaps the most striking result is the emergence of one-shot task adaptation. With the EgoScale prior, a robot can learn a completely new task, such as folding a shirt, from just a single teleoperated demonstration.

This efficiency suggests that the model is not just mimicking motions but has internalized "common motion primitives". This mirrors recent industry shifts toward generative simulation and foundational movement models that prioritize "physical commonsense" over rigid, programmed behaviors.

Embodiment Agnosticism

While the model was primarily trained for the 22-DoF Sharpa dexterous hand, the learned representations proved surprisingly flexible. When transferred to a Unitree G1 robot—which uses a significantly different 7-DoF tri-finger hand—the human-pretrained policy still provided a 30% absolute improvement in success rate over models trained on G1 data alone.

The Galaxea R1 Pro humanoid robot, wearing an NVIDIA cap, uses its dual 22-DoF Sharpa hands to fold a blue t-shirt on a wooden workbench next to a grey basket. — Autonomous dexterity in action: The Galaxea R1 Pro performs the 'Shirt Rolling' task. Using the EgoScale recipe, the robot coordinates two 22-DoF Sharpa hands to fold and roll a deformable t-shirt into a cylindrical shape before placing it into a basket.

This cross-embodiment success supports the "Bitter Lesson" of robot hardware: as robots become more kinematically similar to humans, the need for specialized "transfer" layers disappears. Instead, the rich motion data provided by humans serves as a universal motor prior.

The Path Forward

The EgoScale release arrives amidst an intensifying race to solve the "robotics data gap". While others are collecting high-fidelity teleoperation data at scale, NVIDIA is betting that the "dark matter" of physical interaction is already encoded in the millions of hours of human activity already being recorded.

As model capacity and human data volume continue to scale, the researchers anticipate even greater gains in long-horizon planning and compositional generalization. The ultimate goal remains a "Physical Turing Test"—a world where a machine's physical grace is indistinguishable from a human's.