Physion provides a platform that connects generative AI models to real‑world sensor streams and hardware actuation through a unified API and middleware. The framework lets developers ingest live data from vision, lidar, IMU, and tactile sensors, translate AI outputs into commands for robots or mixed‑reality devices, and synchronize virtual simulations with physical feedback, enabling AI‑driven control in robotics, simulation, and MR applications.
Funding
Funding not disclosed
Founders
Product
Problem
Generative AI models often operate solely on digital data, lacking direct access to real-world sensor inputs and hardware interfaces. This disconnect limits their reliability and usefulness in applications that require interaction with physical environments, such as robotics, simulation, and mixed‑reality systems.
Solution
Physion offers a physical grounding platform that bridges generative AI models with real-world sensor streams and hardware actuation. By providing standardized APIs and middleware, the platform enables AI systems to ingest live sensor data, interpret tangible contexts, and generate outputs that can control physical devices. The solution supports bidirectional communication, allowing AI‑driven decisions to be reflected in hardware actions while feeding back environmental feedback for continuous refinement. Physion’s framework is designed to integrate with existing AI pipelines, simulation engines, and mixed‑reality environments, extending the applicability of generative models beyond purely virtual domains.
Target Audience
Primary customers are robotics developers, simulation engineers, and mixed‑reality creators who need to embed generative AI capabilities into physical or hybrid environments.
Features
- Unified API for ingesting data from diverse sensors (vision, lidar, IMU, tactile) in real time
- Hardware abstraction layer that translates AI outputs into commands for robots, actuators, and mixed‑reality peripherals
- Simulation bridge that synchronizes virtual environments with live sensor feeds for hybrid testing
- Scalable SDK with support for major AI frameworks (PyTorch, TensorFlow) and edge deployment
- Secure data handling and sandboxed execution to protect physical systems from unintended actions