The world changes after deployment.
Task success: best tested policy (π₀.₅) vs expert teleoperation
12.8% vs 100%
RoboDojo, 18 real-world tasks, July 2026A different surface. Changing flight conditions. A new listener or room. When adapting to those changes requires engineers to collect more data, adjust models and redeploy, every new environment adds work.
When production conditions change
Robot arms
The grasp works with the surface conditions in the demo.
Drones
Estimates position under the flight conditions in the demo.
Wearables
Speech is clear for the listener and room used in the demo.
LazyInfer
Adapt whilethe system is running.
LazyInfer estimates what has changed and uses those estimates and their uncertainty to adjust decisions during operation.
We’re packaging this Bayesian inference core as a reusable SDK and runtime, so robotics teams can add adaptation alongside their existing models.
Adaptation under the same changed conditions
Robot arms
Lower friction · the object slips
Drones
New flight conditions · estimates drift
Wearables
New listener or room · speech degrades
Illustrative adaptation
One Bayesian core. Tested across the stack.
Everything the microphone hears.
Learning
Adapting wearable parameters to the user and environment.
GN’s testbed · limited compute, no GPU · paid integration
Built by a team with two decades of research in Bayesian machine learning, computational neuroscience and electrical engineering.
LazyInfer
Bring physical AI from demo to production.
Tell us about your hardware and stack.
We will walk through how LazyInfer fits in.