Motoniq · Embodied AI for the physical world

Next-Generation Robotics: From Physical Experience to Physical Intelligence

The stack that makes AI finally work in the real world.

Today robot demos → policy
Motoniq physical experience → grounding → supervision → intelligence → open to all
1 Sources of
Physical Experience
Robot-Native Trajectories
Human Motion & Sensing
DK-1™Sensorimotor Network
Full-Body Wearable Capture Suit
Tactile· Kinematics· Force
Internet / Egocentric Video
SimulationSynthetic Data at Scale
Language & Task Descriptions
Tactile / Force / Contact Streams
Failures & Deployment Traces
2 Grounding Mechanisms
Shadow™Physics Data Engine
Turns broad experience into robot signals
Embodied Autolabelling
Automatic supervision from raw experience
  • task phases
  • contacts
  • object states
  • latent actions
  • success / failure
Task-Preserving Retargeting
Human behaviour mapped to any embodiment
  • human intent → robot action
  • cross-embodiment transfer
  • skill translation
Omnis™Physics-Grounded World Models
Reasons Within the Laws of the Physical World
  • 3D geometry
  • contact & force
  • symmetries
  • object permanence
  • counterfactual prediction
Task-Conditioned Reward Grounding
Turns experience into learnable objectives
  • task inference
  • progress estimation
  • reward / preference signals
3 Robot-Usable
Supervision
Actions
Object-Centric States
Contacts
Task Phases
Goals
Rewards
Failure Labels
4 Learning & Control
VLA Policies
Skill Libraries
Planning / MPC
Generalist Robot Policies
Built for actions, not pixels
5 Deployment &
Compounding Learning
M-Connect™
Python-native robot framework
Runtime / Deployment
  • deployment layer
  • hardware-agnostic
Physics Simulation
  • PyBullet · MuJoCo · Isaac
  • 3D Gaussian splat engines
Real-World Execution
Human Correction
Continuous Improvement
Self-Learning Loop
Compounding deployment data feeds back to the sources of physical experience
Operated through
6 The Interface — AI as an Intellectual Collaborator
No Engineering Teams Any Operator One Robot, Many Tasks Connect In Minutes

A conversational interface for any machine. The ML team becomes a prompt. The integrator becomes a sentence.

TASK INPUT● UR5e CONNECTED
Pick strawberries from the tray and place them in the punnet on the left.
Reference video attached — "use this video as reference"
● COMPILING POLICY · 247 LOC
MOTONIQ AI
Sure, let's start with "Pick block at pos [0.5,-0.2,0.1]".
Loading Motoniq.
Analyzing task.
Loading skills.
Executing task…
REASONING FROM PRIOR KNOWLEDGE…
TYPE THE TASK…
● 3D LIVE 00:00:24EXECUTING
UR5e · 6-DOF ARM FORCE: 2.4N
SKILL LIBRARY● LIVE
Strawberry Pick TRAINING…
3 DEMOS / FINETUNING
Drone Grab Object DEPLOYED
94% VAL / 10 DEMOS
Sort Items READY
88% VAL / 5 DEMOS
Weld Joint A DEPLOYED
91% VAL / 7 DEMOS
Route Cables DEPLOYED
91% VAL / 3 DEMOS
→ IMPROVING WITH EVERY RUN
Core Thesis
The bottleneck is not only better policies, but mechanisms that transform broad physical experience into grounded robot supervision.