Robotic hand assembling a precision component alongside a human hand
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MORE AI

MoreSight: Spatial Intelligence Software for Robots —

Skilled autonomous robots
— in minutes.

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006 sections
6DoF+v · 30 fps
[ 01 / 06 ] Introduction
Iridescent rainbow glass robot posed as The Thinker
02MoreSight

Your Robots,
Ready for Mastery.

MoreSight

MoreSight is a spatial intelligence model for robots to master precision tasks with minimal training data, resources, and time.

FoundationMORESIGHT SPATIAL INTELLIGENCE

Prime. Train. Execute.

Step 01

Prime with geometry

Don't waste resources training on the entire world. Our models come guided by rules of relevant geometry and physics. We call this priming.

Step 02

Train efficiently

Save GPU cycles by focusing attention during training solely on the skills required to master tasks.

Step 03

Execute with mastery

Realtime spatial state (position, orientation, and velocity of relevant objects in a scene) enables fast edge execution and zero sim-to-real gap.

IntegrationYOUR ACTION AND LANGUAGE MODELS

Integrate your models to
unlock mastery.

Robotic hand assembling a circuit board
+
Focused Action Models

for multisensory execution

Human worker in a hard hat directing a humanoid robot in a factory
+
Small Language Models

to direct intent

Iridescent robotic hand crushing and shattering a glass tumbler as water bursts outward
fig. 03 · pixels
03Pixel Burden

The Pixel Burden

Today's time to skill mastery is too long, or simply never.

  1. 01Tokens

    Books aren't derived from rules — to know them you must read them. LLMs use subword tokenization and must read everything to learn.

  2. 02Pixels

    PI models treat the world like LLMs treat language, tokenizing every pixel. This adds a huge computational burden during training.

  3. 03Failure

    This pixel burden makes it impossible to calculate the position, orientation, and velocity of objects in realtime — the key to efficient skill mastery.

04GINN

The Physical World is Different

Unlike language, the world can be derived from rules of geometry and physics. You don't need to start with pixels.

MORESIGHT

MoreSight is spatial intelligence software that uses geometry and physics to enable rapid skill mastery.

Minimaltraining data·resources·time
GINN

GINN is a neural network that uses depth to extract physics and geometrical invariances from 3D data.

Geometric Invariance Neural Network
05Tokenize Features

Tokenize Features,
Not Pixels

GINN doesn't infer state from pixels. It applies geometric rules to understand the world. This is geometric priming.

0A6DoF+V @ 30 FPS

Geometry is Speed

Geometric features enable output of the position, orientation, and velocity of all relevant objects in a scene — at 30 FPS.

Robotic hand holding a glass of water

Realtime
Spatial State

  • →Precise 6DoF+v
  • →at 30 FPS
  • →any primed geometry
  • →any distance
  • →any lighting conditions
0BFocused Attention

Geometry is Focus

Tokenizing features lets machines focus 100% of training attention on rapid skill mastery with just edge resources.

Dining table where plates, glasses, bottle, and cutlery are rendered as glowing cyan wireframe geometries while the rest of the room stays photorealistic

MoreSight wastes no attention on what has already been primed and understood:

  • Objects
  • Physics
Metallic robot with glowing green eyes assembling mechanical parts
06Efficient Attention

Efficient Attention

Attention is applied on top of geometric features, with exclusive focus on skill mastery:

  • semantic and natural context
  • sequence and interactions
  • desired end state

Priming + focused attention = 0 sim-to-real gap

fig. 05 · focused execution

Spatially intelligent robots, ready for skill mastery.

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