WORLD
ACTION
MODEL

Learning physical intelligence through observation and interaction — enabling robots to understand and act in the real world, and generalize across tasks, environments, and embodiments.

Interaction–Driven Learning

From Physical Interaction
to World UNDERSTANDING

Learning physical intelligence through real-world interaction — enabling robots to manipulate, adapt, and generalize across tasks, environments, and embodiments.

TACTILE-DRIVEN REINFORCEMENT LEARNING BEYOND SPARSE REWARDS NEW DATA (REAL INTERACTION DATA) MODEL WEIGHTS EFFICIENCY + ACCURACY CONTINUAL LEARNING Tacitle feedback provides what other reward signals cannot: dense, contact-grounded physical ground truth at the moment of interaction. 
Layer 1
Data & Training

Large-Scale Observation

Efficiency
Pre-training

Diverse robot operation videos, human demonstrations, and proprioceptive recordings — providing broad priors about objects, motion, and environmental dynamics.

Human Demonstrations
Videos
Language
Proprioception

Tactile-Augmented Simulation & Training

Efficiency

Simulation generates diverse interaction variants at scale — augmented with tactile signal modeling to better reflect real contact dynamics.
Real-world tactile data from Sentra continuously recalibrates simulation parameters, ensuring virtual training remains grounded in physical reality.

Real Interaction Data

Accuracy
Pre & Post-training

High-fidelity tactile interaction data capturing force dynamics, contact transition, and material response — the physical ground truth that vision cannot provide.

Contact Force Maps
Tactile Deformation
Material Response
Slip Detection
3D Force Signals
Layer 2
Trained Models

World Action Models

PREDICTIONS GROUNDED IN HOW THE WORLD CAN BE ACTED UPON
NOT JUST HOW IT LOOKS

Inputs

Vision
Tactile
Language
Proprioception

Outputs

Physical Future Prediction
Continuous Action Policy
Layer 3
Deployment Outcomes
ENVIRONMENT-INVARIANTMANIPULATION

In contact-rich tasks, tactile signals provide continuous feedback on force, slip, and material response — revealing actionable affordances that guide stable execution through every phase of contact.

Few-ShotCross-Task

Physical interaction follows causal laws — not visual patterns. By grounding models in tactile physics, skills learned in one context transfer to unseen objects and environments without task-specific retraining.

Cross-Embodiments

Force, compliance, and contact dynamics are the universal language of physical interaction. Models trained on one platform transfer across robot morphologies — reducing deployment cost for every new embodiment.

OPEN

MODEL
WORLD
SKILLS
INTENT-DRIVEN
MANIPULATION

Robots to perceive, plan, and act from intent — without scripts or task-specific programming.

ADAPTIVE
TACTILE INTERACTION

Millisecond tactile feedback stabilizes manipulation behavior in real time.

BUILT-IN SKILL
PRIMITIVE

Composable skill units designed for modular scaling across tasks.

OPEN SKILL
ECOSYSTEM

A shared skill library where manipulation intelligence accumulates, is reused, and continuously evolves across deployments.

OPEN MODEL...
OPEN SKILLS...
OPEN WORLD...
Robot for Science
1
Robot for Science
Precision Engineering
2
Precision Engineering
Scalable Services
3
Scalable Services
Consumer Robotics
4
Consumer Robotics

TURNING
PHYSICAL
INTELLIGENCE
INTO REAL–
WORLD IMPACT