Physical AI refers to robotic systems that learn from and act in the physical world. It connects AI models with real-world motion, contact, force, torque, sensor feedback, and task outcomes. 

The physical interaction layer is the hardware and tooling that connects AI models to real-world action. It includes grippers, force/torque sensors, end-of-arm tools, and feedback systems that allow robots to grasp, manipulate, measure contact, and capture useful observations during execution. 

AI-ready physical observations are signals from real-world interaction that can enable model training, validation, and deployment. Examples include force, torque, gripper width, proximity, grip state, tool state, part detection, errors, and task outcomes. 

OnRobot grippers such as RG2/RG6, 2FG7/2FG14, and RG2-FT can support Physical AI applications where flexible, programmable, real-world manipulation is needed. These tools can help teams test how models perform with different objects, part sizes, contact conditions, and task outcomes. 

HEX force/torque sensors can help robots measure physical interaction during tasks where contact, alignment, resistance, or applied force matters. This can support validation, feedback control, and the capture of real-world observations for manipulation tasks. 

Because many manipulation tasks depend on what happens during contact. Force and torque data can help robots understand grip stability, resistance, slip, insertion force, alignment, and failure conditions. 

RG2-FT combines electric gripping with integrated force/torque and proximity sensing. That makes it useful for collecting contact-rich manipulation data and validating robotic behavior in real-world tasks. 

Learning from demonstration depends on high-quality examples of real actions and outcomes. RG2-FT can help capture physical interaction signals during demonstrations, including force, torque, position, proximity, and task results. 

OnRobot tools allow robotics teams to compare expected behavior with real-world outcomes. By testing robot performance under physical conditions, teams can evaluate how models perform when objects vary, contacts shift, and real manipulation introduces uncertainty. 

Depending on the tool and setup, OnRobot products can provide feedback such as force, torque, position, proximity, tool state, part detection, grip state, and errors. 

Electric grippers are programmable, controllable, and feedback-capable. That makes them well suited to repeatable data collection, adaptive manipulation, and real-world validation. OnRobot’s electric grippers also have the flexibility to handle parts of different sizes, helping teams test how algorithms respond to real-world variability. 

No. OnRobot tools and grippers are designed for real-world industrial automation tasks. More than 22,000 OnRobot products have been deployed globally. For Physical AI teams, that means training and validation can happen with hardware built for real-world use. 

Yes. Where reliable, electric, feedback-rich end-of-arm tooling is needed, OnRobot tools can support humanoid and mobile manipulation teams working toward practical deployment.