Physical AI · Dependability Layer
Physical AI
for the real world.
Foundation models can already fold a shirt. Videron makes them robust enough, usable enough, and cheap enough to run — so robot policies can finally leave the lab.
Full run, unedited · one take, no cuts
The problem
A folded shirt is a demo. A folded shirt every time is a product.
The bottleneck has moved. Capability is being solved by foundation models. Dependability — knowing when to trust them, letting people direct them, running them affordably — is not.
Silent failure
A VLA acts with the same confidence on a scene it has never seen as on one it has. There is no built-in signal that says "I am outside my training distribution" — so the first sign of failure is a ruined fold.
No user in the loop
Policies are steered by hand-tuned prompts, scripts and a terminal. The person who actually needs the task done has no safe, natural way to ask for it — or to correct it mid-task.
Compute costs
A general VLA is enormous relative to the narrow job it is doing. Running one 24/7 per station is the single biggest obstacle to putting physical AI anywhere real.
Cloth is the hardest cheap benchmark in manipulation: deformable, self-occluding, an effectively infinite state space. Every failure mode we care about — out-of-distribution scenes, ambiguous instructions, compute pressure — shows up in a laundry basket long before it shows up in a warehouse.
Mission
The layer that makes physical-AI policies dependable enough to ship.
We are not training another foundation model. We make the ones that exist survive contact with a real room, a real user, and a real compute budget.
Robustness
Know when the policy is out of distribution — and manufacture the data that closes the gap.
Interface
An agent + voice front end and an MCP control surface so a non-expert can direct the policy.
Deployment
Quantization, distillation and routing so each task runs on the smallest model that can do it.
The system
From a spoken instruction to a closed robustness loop.
Team & company
Small, technical, and building prototypes already.
Tyler Staudinger
Tyler has been working with neural networks for over two decades to create autonomous vehicles, robots, drones, and aircraft. He holds over 16 granted US patents in machine learning and aerospace, and a Master's degree in Electrical Engineering.
His passion for physical AI stems from a lifelong interest in robotics and neural networks. He built the full Videron stack solo: hardware integration, data collection, policy fine-tuning, evaluation, and deployment.
Get in touch
We have the testbed. Let's make it dependable.
Looking to connect with design partners who have real manipulation workloads, and with teams working on robustness, simulation and edge deployment for physical AI.