Midcentury

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

We post-trained @physical_int's π0.5 on our ego data and found it outperformed Apple’s EgoDex on LIBERO-10.

Equal amounts of data were sampled from both datasets with matched tasks, environments, and objects.

Our results suggest that viewpoint diversity and richer action supervision can overcome a fundamental limitation of robot learning: dependence on fixed camera positions.

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.

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.

Announcing the Midcentury Research Residency.

We're bringing together a small cohort of researchers to tackle the hardest problems in physical AI, backed by:

→ $1 million in compute
→ The world's largest robotics dataset spanning 2M+ hours
→ SOTA action supervision infrastructure

Our first cohort includes an author of NVIDIA's Cosmos 3, a CoRL award winner, and researchers from CMU, UCL, and Brown, with publications at NeurIPS and ICRA.

Their research focuses on fundamental questions in robot learning and world models. We've already published MC-PhysBench, with more work coming soon.

Applications for Cohort 2 are now open. Apply here: midcentury.xyz/residency