@rohanpaul_ai: Physical AI teams should be able to test a new release the way software teams run a test suite. simulation must become …

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Summary

Antioch Robotics raises $32 million in Series A funding to build simulation infrastructure that enables Physical AI teams to test releases like software teams, integrating simulation into CI/CD for reproducible and scalable testing.

Physical AI teams should be able to test a new release the way software teams run a test suite. simulation must become trustworthy enough to act as a release gate for physical AI. @antiochrobotics just raised a $32 mn Series A to narrow that gap. The company is building simulation infrastructure where the scenario itself becomes a software object. You can write a test locally in Isaac Sim or Isaac Lab, define parameter distributions, then fan that scenario out across cloud GPUs. More importantly, those simulations can be connected directly to CI/CD. In robotics, the bug may depend on a weird combination of wind, lighting, geometry, sensor noise, human traffic, or contact dynamics. Turning that exact physical state into a repeatable test is much harder. But with simulation, when a robot fails in deployment, you can reproduce the same failure in simulation, vary the conditions around it, and keep those tests for every release after that.
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Cached at: 09/08/26, 07:38 PM

Physical AI teams should be able to test a new release the way software teams run a test suite.

simulation must become trustworthy enough to act as a release gate for physical AI.

@antiochrobotics just raised a $32 mn Series A to narrow that gap.

The company is building simulation infrastructure where the scenario itself becomes a software object.

You can write a test locally in Isaac Sim or Isaac Lab, define parameter distributions, then fan that scenario out across cloud GPUs. More importantly, those simulations can be connected directly to CI/CD.

In robotics, the bug may depend on a weird combination of wind, lighting, geometry, sensor noise, human traffic, or contact dynamics. Turning that exact physical state into a repeatable test is much harder.

But with simulation, when a robot fails in deployment, you can reproduce the same failure in simulation, vary the conditions around it, and keep those tests for every release after that.

Antioch (@antiochrobotics): Today, we’re announcing Antioch’s $32 million Series A, led by @GreylockVC with participation from @A_StarVC, @Category_VC, @BoxGroup, @IcehouseVenture, and angels.

While AI has drastically accelerated software development, physical autonomy has been constrained by slow,

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The State of Simulation for Physical AI: An Overview

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This blog post by NVIDIA provides an overview of simulation engines for physical AI, covering why simulation is critical for robotics data generation and comparing tools like MuJoCo, Isaac Sim, and Newton. It highlights the shift from simulation as a debugging tool to an integral part of model development.

@RemiCadene: Really cool startup :)

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Antioch Robotics announces a $32 million Series A funding round led by Greylock VC to advance AI for physical autonomy.