Renesas frames this demo around sensing as a core building block for edge AI, robotics, mobility, and industrial automation. The focus is not on one isolated component but on how force sensing, position sensing, impedance sensing, and low-footprint embedded intelligence can be combined into compact actuator and HMI designs that are precise, robust, and realistic to scale in production. https://www.renesas.com/IPS
The robotic hand is a good example of that direction. Instead of simple fingertip touch, the demo shows full-finger force measurement, so grip strength and the force curve over time can be tracked as the grasp develops. That matters for dexterous manipulation, safe human-robot interaction, and more natural motion control, where the system must regulate pressure finely enough to hold fragile objects without instability or slip.
A second theme is robotic joint feedback. Renesas positions inductive, magnet-free sensing as a practical fit for humanoid and industrial robot joints because it can deliver absolute position information, high resolution, immunity to stray magnetic fields, and better robustness against moisture, vibration, dust, and electromagnetic disturbance. That lines up with the company’s newer inductive position sensor push, including parts such as the RAA2P3226 for robotic joints, where compact integration, low latency, and tight angular accuracy are critical for servo control and coordinated motion.
The mobility demo extends that sensing approach into the human-machine interface. The scooter handle detects whether both hands are present using impedance sensing rather than conventional capacitive touch, which improves operation with gloves and in humid or wet conditions. Renesas is also emphasizing more complete reference algorithms around these sensors, so OEMs can tune sensitivity and recognition behavior in software without starting from scratch, which is often what product teams need when time-to-design is tight.
The final part of the video is about edge intelligence in a more literal sense: sensor data processed locally on a modest 32-bit microcontroller to infer things that are not directly measured, such as leakage, friction, or load change for predictive maintenance. That is a useful distinction in industrial sensing because it keeps latency, memory demand, power budget, and system cost under control while still enabling condition monitoring. Filmed at Embedded World 2026 in Nuremberg, the demo shows Renesas pushing sensors beyond raw measurement toward embedded perception for robotics, micromobility, and Industry 4.0.
