Infrastructure for
Physical AI
Bringing AI from simulation into the real world.
Asymmetri builds infrastructure that enables AI systems trained in simulation to operate reliably in noisy, constrained physical systems.
The sim-to-real gap
is where systems fail.
Training in simulation is efficient. Deployment is not. Sensors drift, environments shift, and assumptions collapse under real-world uncertainty.
Simulation is scalable, cheap, and safe for training. Reality adds hardware variability, latency, and partial observability. Policies that perform well in simulation often fail at transfer.
This is why most AI systems never leave simulation.
From training to transfer to deployment.
A stack designed to move learned capability into production physical systems.
Training Layer
Build high-throughput simulated environments, RL pipelines, curricula, and multi-agent training loops.
Transfer Layer
Define the contracts that let policies survive transfer: observation mappings, action surfaces, noise models, and calibration.
Deployment Layer
Operationalize physical AI with runtime deployment, sensor integration, edge inference, and live monitoring.
Develop policies where iteration is cheap, safe, and data-rich.
Make the move into reality measurable, auditable, and resilient.
Operate against real sensors, hardware constraints, and continuous feedback.
AI is becoming capable enough to act. The missing infrastructure is what lets that capability survive contact with the physical world.
Designed as foundational infrastructure for real-world autonomous systems.
Sim-to-real is the product boundary
Transfer and deployment are treated as core infrastructure problems, not edge cases after training.
Infrastructure mindset
The goal is a repeatable layer for physical AI systems, not isolated robotics demonstrations.
Built for harder environments
Decentralized multi-agent systems are an early proof point because they expose coordination, noise, and runtime complexity quickly.
A harder test case for infrastructure robustness.
Asymmetri's early systems include decentralized multi-agent deployments, where policies trained in simulation are transferred into real-world robotic agents. These environments are intentionally demanding.
Coordination must emerge under uncertainty, noise, and hardware constraints. We view this as evidence of platform strength, not the limit of the company.
Policies coordinate through shared environments rather than centralized control.
Sensor noise, timing drift, and partial observability force stronger transfer assumptions.
Runtime systems must manage many agents, not a single isolated policy endpoint.
Success here suggests infrastructure that can generalize to broader physical AI categories.
The next frontier of AI is not more software. It is intelligence operating in the physical world.
Much of modern AI still lives in digital environments. The next wave depends on systems that can perceive, adapt, coordinate, and act under real-world conditions.
Asymmetri aims to build the infrastructure that makes that transition possible: rigorous enough for deployment and extensible enough for future classes of autonomous systems.
The missing layer between simulation and reality.
For early collaborators, research conversations, and deployment partners.
Asymmetri is an early research company building infrastructure for sim-to-real AI. If you are working on real-world autonomous systems and the transfer problem matters to you, we should talk.
Vancouver, BC V5Z 1C6
Canada