Real-world egocentric data and high-fidelity simulation at scale for training, evaluating, and deploying robots.
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.
Large-scale robotics and simulation data, paired with rich action supervision and dense annotations for the next generation of physical AI.
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.
Head and wrist recordings of gripper tasks: RGB and stereo cameras, IMU, fused head and tool-centre-point pose, and gripper state.
Agentic simulation platform to design, test, and train physical AI across massively parallel cloud environments.
Latest · Research · Oct 27, 2026SortexScreening, annotating, and filtering the world’s largest egocentric dataset for specific training needs.
Research · Read moreAkariPhotorealistic rendering for classical physics engines, trained to close the sim-to-real gap.
Research · Read moreMC‑PhysBenchHow long video world models stay physically valid against simulated ground truth.
Research · Read moreMC‑EgoHandsA frontier 3D harness for human egocentric video.