Midcentury

Data and Simulation Infra for Physical AI

Real-world egocentric data and high-fidelity simulation at scale for training, evaluating, and deploying robots.

DatasetsSimulation

01 Mission

Physical AI produces impressive demos. Yet very few robots ever reach the real world.

Scaling robotics now requires:

Human action data at internet scale for pretraining.

Reliable simulation for evaluation and RL.

Midcentury builds both.

The next industrial revolution begins when robots leave the lab.

02 Datasets

High-Fidelity Data for the Real World

Large-scale robotics and simulation data, paired with rich action supervision and dense annotations for the next generation of physical AI.

Egocentric Vision

5 Environments · Head-Mounted
Hand pose · 2D overlay
DetailsSamples, specs and coverage

One of the largest unscripted egocentric datasets across industrial and everyday environments. Video, IMU, and audio enriched with 3D hand pose, point tracks, depth, and task annotations.

2M+
Hours
50+
Environments
3D Pose
+ Annotations

Custom Gameplay Environments

4 Environments · Top-Down
Depth pass
Speed · m/s3.0Turn Rate · deg/s-5
Telemetry
DetailsCapability library & sample spec

Custom, on-demand gameplay data from environments built to spec — any camera angle from first-person to top-down — with frame-aligned inputs, telemetry, camera state, and engine G-buffers.

50K+
Hours Supported
100+
Environment Types
Engine-Level
Signals

03 Simulation

Midcentury Matrix

Agentic simulation platform to design, test, and train physical AI across massively parallel cloud environments.

01 Design

Digital twins from real deployment conditions, combining classical simulation with learned physics.

02 Test

Massively parallel GPU evaluation across thousands of scenarios. Replay failures, catch regressions before hardware.

03 Train

Turn failures into new scenarios and training experience. Feed real-world data back into policies.

04 Research

Our Contributions to Physical Intelligence.

See all