≈1s
CPU model fitting
0.83–1.16 seconds per Meta-World task model, fitted from prepared state and action data.
Efficient Intelligence.
Deterministic Action.
Loading the robot motion study.
Spend less effort maintaining everything around it.
≈15
Teacher demonstrations
18 demonstrations in the recorded Panda experiment, using structured sensor observations.
0
GPUs required
CPU-only model fitting and control computation in the tested experiments.
≈100MB
Peak RAM
Peak memory use for robot control and visual processing.
Learn the skill. Adapt the action.
A shorter path
to deployment
Build and test decision workflows in one environment, then run them on the same engine near your robot. Spend less time reconnecting logic between prototype and deployment.
Adapt without
starting over
Reuse learned models and refine behavior with new observations and feedback. Reduce repeated training time and cost when tasks, conditions or operating parameters change.
One engine.
A simpler stack.
Bring learning, reasoning, optimization and verification into one licensed runtime. Reduce the separate tools and integrations you maintain in the decision layer.
Behavior you can
inspect and replay
Examine inputs, state and execution traces to understand how a decision was reached. Repeat runs against fixed inputs and state to investigate unexpected behavior.
Evidence for
safety review
Make operating constraints explicit and retain the reasoning behind decisions. Give engineering and compliance teams evidence for safety and regulatory reviews.
CPU-first.
Compact by design.
A compact CPU-first runtime helps keep memory, compute and energy overhead under control, without requiring a GPU cluster for the decision layer.
Teaching robots has never been easier.
Install the Holo Engine MCP and connect it to your AI assistant. Describe the skill you need, and let the assistant guide the training workflow.
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Your task
Describe the behavior, the goal and the operating constraints.
-
Connects to Holo Engine through MCP, with results returned to the assistant.
AI Assistant
Builds the training workflow, calls engine tools and checks the results.
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Demonstrations & sensor data
Holo Engine
Learns task models from demonstrations and sensor data.
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Learned skills
Save and reuse learned skills. Refine them as the task changes.
Combine functions to expand your robot’s capabilities.
Holo Engine brings together more than 30 functions, from discovering hidden patterns to solving SAT problems. Combine them to extend what your autonomous systems can do.
Cognition
Recognize the load and contact state from sensor features.
Reason
Estimate how the grip responds to the observed load.
Optimize
Choose a grip setting within the stated force limits.
More skills. Same robot.
Add new skills, reuse what’s learned, and adapt to changing tasks—without starting from scratch.
Growing skill library
One AI engine. Three ways to use it.
Choose the integration that fits your robot and your existing stack. Pricing is tailored to each project.
Action
Verification
Add a verification layer to your existing VLA or neural control stack. Check proposed actions against explicit constraints before execution.
Specialized
Skills
Teach your robot task-specific skills and integrate them into your existing control system.
Full-Cycle
Intelligence
Bring learning, reasoning, action selection and verification together in one runtime tailored to your robot.
Every robot. A measurable outcome.
Tell us what your robot needs to do. Let’s discuss a pilot.