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@MidcenturyAI • 2,733 subscribers

Data and Simulation Infrastructure for Physical AI

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Introducing Akari: a frontier learned rendering model for robotics simulation. Akari generates photorealistic rendering for any physics engine to close the sim-to-real gap. Trained in-house, it is the fastest and highest-fidelity learned renderer in any sim platform: 0.046 LPIPS at 0.5s/frame, consistent across 1K+ frames.

Introducing Akari: a frontier learned rendering model for robotics simulation. Akari generates photorealistic rendering for any physics engine to close the sim-to-real gap. Trained in-house, it is the fastest and highest-fidelity learned renderer in any sim platform: 0.046 LPIPS at 0.5s/frame, consistent across 1K+ frames.

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Introducing Midcentury. We’re building the data and simulation infra for physical AI. Today, we’re coming out of stealth with a $15M Series Seed to scale robotics beyond polished demos. We’re already supporting frontier labs with: → The world’s largest egocentric dataset: 2M+ hours, 50+ environments, 20,000+ tasks → Matrix: a frontier simulation platform scaled with our real-world data to evaluate and post-train policies at scale

Introducing Midcentury. We’re building the data and simulation infra for physical AI. Today, we’re coming out of stealth with a $15M Series Seed to scale robotics beyond polished demos. We’re already supporting frontier labs with: → The world’s largest egocentric dataset: 2M+ hours, 50+ environments, 20,000+ tasks → Matrix: a frontier simulation platform scaled with our real-world data to evaluate and post-train policies at scale

642,457 views • 1 month ago

Sortex: our data quality platform built to search and filter the world's largest egocentric dataset. Across 50+ environments and 20k+ tasks, it uses an in-house model and evaluation harness to score each clip's usability, selecting for specific training objectives by: → Narrating tasks and steps → Checking hand and action visibility → Comparing against the rest of the corpus We also tested Sortex on popular open datasets and found they differ widely in quality and coverage.

Sortex: our data quality platform built to search and filter the world's largest egocentric dataset. Across 50+ environments and 20k+ tasks, it uses an in-house model and evaluation harness to score each clip's usability, selecting for specific training objectives by: → Narrating tasks and steps → Checking hand and action visibility → Comparing against the rest of the corpus We also tested Sortex on popular open datasets and found they differ widely in quality and coverage.

16,368 views • 14 days ago

How do you scale 2 million hours of ego data without sacrificing quality? Earlier this week, we revealed the world’s largest egocentric dataset. Our frontier pipeline has also turned it into the most labeled, annotated, and human QA’ed robotics ego dataset in the world. We’ve focused on some of the hardest problems in computer vision and ego data to ensure every clip contains SOTA action supervision directly helpful for training dexterous manipulation.

How do you scale 2 million hours of ego data without sacrificing quality? Earlier this week, we revealed the world’s largest egocentric dataset. Our frontier pipeline has also turned it into the most labeled, annotated, and human QA’ed robotics ego dataset in the world. We’ve focused on some of the hardest problems in computer vision and ego data to ensure every clip contains SOTA action supervision directly helpful for training dexterous manipulation.

10,802 views • 1 month ago

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