We develop and deploy intelligent machines at scale and at unprecedented speeds. An approach based on building end-to-end robotic workcells powered by bleeding-edge AI algorithms and dedicated hardware and supported by powerful simulation-to-reality engines, is the only way to realize real production-grade machines operating at line speeds.
Our product offerings are underpinned by RIOS-Net – a unique technology stack that consists of 500+ repos of proprietary software, simulation backends, data management infrastructures, and dedicated hardware, which not only contribute to a premier customer experience, but are also real barriers to entry.
Hardware & Software Innovations
Digital Twin Framework
Haptics Intelligence Platform
Engineered the world’s most advanced tactile sensors for robots, and pioneered haptic intelligence for robots, enabling robots to have a human-like sense of touch.
Engineered a new class of highest performance end-of-arm tooling, enabling our robots to emulate human dexterity and stack up to manipulating objects at human speeds. RIOS has built industry-leading food-grade grippers for handling delicate food items.
AI is at the core of the robotic wokcell. We’ve developed a broad range of unique and bleeding-edge algorithms for object recognition, pose determination, grasp points computation, and decision-making. RIOS has also developed its own unique flavor of imitation learning and reinforcement learning algorithms that is tied to its dedicated hardware.
Architected the most powerful synthetic data generation platform and robotics simulation backends that model realistic virtual environments for our robotic workcells. It allows us to import CAD drawings, realistically simulate and test our robotic workcells in a virtual world and export the workcell designs directly to our supply chain network for fabrication and assembly, all done at unparalleled speeds.
Data Management Platform
Data is a centerpiece of intelligent machines. We’ve developed hybrid cloud platform for accessing and manipulating terabytes of data, building engineering pipelines, standardizing ML models, and more.