THE CONTINUOUS LEARNING LAYER FOR INDUSTRIAL ROBOTS

Realtime learning for industrial robots.

Square Hammer turns every task, test, and deployment into better robot behavior. Each robot starts with what our platform has already learned.

Request early access

See how robots learn

Available now

Diagnostic and fix engine.

Give Square Hammer the task, your robot data, and each result. The engine finds what blocks the task, chooses the right change, proves the result, and returns the fix.

01 · FIND

Find why it failed.

The engine reads your robot’s own signals: torque, contact, control, and outcomes.

02 · FIX

Choose the right fix.

A fix can change code, parameters, or training. The engine picks the fix that matches the failure.

03 · PROVE

Prove the fix works.

The engine replays the failure as a test. The fix must pass before release.

04 · RETURN

Put it back to work.

Deploy through controlled release. Keep behavior that already works.

YOU PROVIDE

Task, robot data, and software.

SQUARE HAMMER

Finds the cause, chooses the change, and proves the result.

YOU RECEIVE

A validated code, model, parameter, training, or process change.

Tell us what your robot must do.

CUSTOMER DEVELOPMENT RESULT

CUSTOMER RESULTS

In a customer evaluation, Square Hammer automatically resolved 74% of robot failures.

Vision-only diagnostics resolved 0% of the same cases.

THE LEARNING LAYER

Turn robot experience into shared intelligence.

Your tests, failures, corrections, and successful runs contain useful knowledge. Square Hammer makes that knowledge usable across your fleet.

New work

Teach your robot useful work.

Create the first working behavior from the methods, examples, software, and controls your task needs.

Real conditions

Adapt your robot to the real world.

Adapt validated behavior when lighting, parts, tools, operators, or sites change.

Right action

Choose what you must change.

Choose the code, model, control, hardware, training, or process change that improves the task.

Shared learning

Transfer what works across your fleet.

Release validated behavior and transfer it to the next task, robot, and site.

FROM FIRST TASK TO EVERY DEPLOYMENT

One continuous loop for robot learning.

Your robot does not start from zero. Square Hammer teaches the task, proves that it works, adapts to new conditions, and shares the result across your fleet.

01Define

Set your task, your conditions, and the result you need.

02Recall

Start with every fix and behavior Square Hammer learned from tasks like yours.

03Learn

Use your examples, data, software, and models to teach the behavior.

04Try

Run your robot in simulation, in the lab, or at a site.

07Prove

Measure your result and keep behavior that already works.

06Improve

Change your model, software, control, hardware, or process.

05Understand

Find what moves your robot toward the task and what blocks it.

01Define

Set your task, your conditions, and the result you need.

02Recall

Start with every fix and behavior Square Hammer learned from tasks like yours.

04Try

Run your robot in simulation, in the lab, or at a site.

03Learn

Use your examples, data, software, and models to teach the behavior.

Each robot starts with what our platform has already learned.

Validated behavior

Diagnostic memory

Reusable lessons

05Understand

Find what moves your robot toward the task and what blocks it.

06Improve

Change your model, software, control, hardware, or process.

07Prove

Measure your result and keep behavior that already works.

01Define

Set your task, your conditions, and the result you need.

02Recall

Start with every fix and behavior Square Hammer learned from tasks like yours.

03Learn

Use your examples, data, software, and models to teach the behavior.

04Try

Run your robot in simulation, in the lab, or at a site.

Each robot starts with what our platform has already learned.

Validated behavior

Diagnostic memory

Reusable lessons

05Understand

Find what moves your robot toward the task and what blocks it.

06Improve

Change your model, software, control, hardware, or process.

07Prove

Measure your result and keep behavior that already works.

RESEARCH THAT POWERS YOUR LEARNING LOOP

Four research programs power every learning cycle.

Your robot must learn what matters, use the right signal, choose the next change, and prove that it still works. Square Hammer brings these four fields into every learning cycle.

Grounded behavior

Know what matters in real work.

Square Hammer connects each result to the model, controller, data, hardware, or work process that produced it. Your team sees what changed the task.

Learning signals

Turn each attempt into the right learning signal.

Square Hammer combines video, robot state, contact, corrections, and outcomes. Your models receive the data they can use.

Fleet decisions

Choose the next improvement.

Square Hammer ranks changes by effect, safety, cost, and time. Your team works on the highest-value action first.

Continuous validation

Prove behavior as the world changes.

Square Hammer tests each change against prior tasks and new conditions. Your team detects drift before your customer does.

Use the methods and systems you already have.

Square Hammer connects classical control, AI models, simulation, reinforcement learning, and code. You can keep your current hardware and training methods.

Foundation policies

RT-1RT-2RT-XOpenVLAOctoπ0π0.5GR00TGemini RoboticsHelixSmolVLAForceVLAWholebodyVLAACT-2LingBot-VA-2.0

World models

CosmosV-JEPA 2Dreamer v3TD-MPC2GenieVideo WMs

Simulation

Isaac LabDomain RandomizationDigital TwinsASPIREENPURESim2Real Co-TrainingZero-Shot S2R

Imitation and data

ALOHADiffusionUMI HandheldDROIDOpen-XMimicGenDexMimicGenSensorized-HumanTeleop

Post-training and RL

RLTOnline RLOffline RLHIL-SERLRLPDResidual RLDAggerTT AdaptationLoRA FTReward ModelsChunkFlow

Agentic and code

EurekaDrEurekaCode-As-PolicySayCanLLM Rewards

Classical controls

Param TuningMPCMPPIImpedence tuningMotion re-planningGrasp librariesVision Re-calPID Re-tune

WHO ARE WE?

We build systems that must work. We built distributed and machine-learning systems at CERN, Salesforce, Uber, Meta, Amazon, and Apple.

Request early access