From the source
Frontier Embodied Reasoning for large-scale robotic data annotation July 9th, 2026 Perceptron Egocentric API Frontier Embodied Reasoning for large-scale robotic data annotation July 9th, 2026 Perceptron Egocentric API Frontier Embodied Reasoning for large-scale robotic data annotation Perceptron Egocentric turns raw robot and egocentric video into structured, policy-trainable supervision: temporal segmentation into atomic manipulation events, self-contained subtask labels, and dense per-hand grounding signals.
Today we are opening early access for robotics labs, researchers, and data providers building large-scale annotation pipelines.
Why it matters for robotics labs Policy training is bottlenecked on supervision.
Video is cheap to collect and expensive to label: human annotation runs roughly $50 per video-hour, and automated pipelines built on general-purpose VLMs produce flat text labels with weak temporal boundaries and no grounding in what the hands did.
Perceptron Egocentric closes that gap.
It outperforms Gemini Robotics ER-1.6 and Gemini 3.5 Flash, the previous state of the art for this domain, and has been tested extensively on our internal use-cases.
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