Spatial memory for embodied AI

A memory of the physical world, kept in measure.

具身智能的空间记忆数据引操

“We don’t build the robot. We build its experience.”

The thesis

We don’t build the robot.
We build its experience.

Embodied intelligence is bottlenecked not by models, but by experience — the scarce, physically-grounded record of how the real world is actually handled.

01 — The problem

Embodied AI is starved of real-world data.

Models have scaled. The data that grounds them in physics has not.

  • 01 Internet video has no body No force, no depth, no first-person dexterity.
  • 02 Simulation lacks reality The sim-to-real gap remains the wall.
  • 03 Real data is locked away Trapped in factories, fragmented, non-portable.
02 — What we do

From real-world capture to physically-aligned experience.

Dual-modal acquisition feeds a metric physical reconstruction, closing the loop with training and evaluation.

Step 01

Capture

Dual-modal acquisition — Teleport teleoperation and EgoWear first-person wearables. Two hands, real tasks.

Step 02

Reconstruct

4DGS physical reconstruction — a metric, time-aligned twin of the scene, grounded in real geometry and force.

Step 03

Train & evaluate

Closed-loop policy training and benchmarking, returning signal that targets the next capture.

03 — The flywheel

The loop that compounds.

Each turn makes the next cheaper and the data more valuable. The flywheel, not any single model, is the durable advantage.

Capture · Reconstruct · Train · Repeat.

04 — Why us · the moat

A position no robot maker can hold.

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Neutral third party

We don’t compete with our customers’ robots, so they trust us with the data. Compliance-ready, data stays in-region.

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4DGS physical grounding

Metric, physically-aligned reconstruction — not loose video. The texture of reality, made trainable.

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Open schema

The USB-C for embodied data — one standard every rig and model plugs into. We own the interface.

05 — Beachhead

The automotive Tier-1 factory floor.

Vision-led, two-handed assembly — the dexterous tasks robots can’t yet do, and where real experience is most scarce. A controlled environment, a measurable outcome, a repeat customer.

Engagement loop
01Deploy capture on the customer’s line
02Reconstruct & train on their tasks
03Return a usable policy — data stays in-region
04Expand to the next task, next line
See it run

Filmed, not rendered.

Teleoperation, 4DGS reconstruction, and annotate-at-capture.

Demo reel — drop a video here
Host on Cloudflare R2 / Stream, link the URL — don’t commit the file
What anchors us
i.

Memory & space

Mnesis — from the Greek for memory. A 4DGS physical twin that remembers the world in metric measure.

ii.

Real-world data

Factories, two hands, first-person. The scarce, physically-grounded experience models can’t scrape.

iii.

Closed-loop flywheel

Capture, reconstruct, train, evaluate, recapture — a compounding advantage that out-runs any one model.

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Investors · Series Seed · 2026

Memory for machines.

Mnesis Labs is building the data layer that embodied intelligence is currently missing. If you invest in robotics, spatial computing or data infrastructure, we’d like to talk.

Contact

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