Perception
Cameras, sensors and signals provide information about the physical environment.
Robots
AI is moving from answering questions to perceiving, deciding and acting in physical environments. Robotics connects models with sensors, machines, simulation, safety and people.
From digital to physical
Cameras, sensors and signals provide information about the physical environment.
Models interpret information, goals, context and constraints.
Software chooses a sequence of actions while respecting limits and objectives.
Robots, actuators, vehicles or machines interact with the physical world.
Sensors and people confirm outcomes, detect exceptions and trigger recovery.
Logs, versions, sensor data and interventions help reconstruct behavior and improve the system.
Experiment before deployment
Simulation allows teams to test movement, perception, workflows and edge cases before exposing equipment or people to unnecessary risk.
Open ecosystems can also reduce the barrier to experimentation by combining reusable hardware, software, models and datasets.
The physical world adds friction — and that friction is exactly why testing matters.
What we explore
Models that connect perception, reasoning and action in embodied systems.
Manipulation, repetitive work, assistance and human-robot collaboration.
Detection, inspection, localization and environmental understanding.
Connect machines, sensors and local computing where latency or connectivity matter.
Model equipment and environments for simulation, training, monitoring and experimentation.
Explore interoperable software, hardware and data approaches that reduce dependence on one ecosystem.
Related insight
Physical AI turns model errors into possible physical consequences. That changes testing, safety, permissions, human oversight and evidence.