This video tours Renesas demos focused on vision AI robotics and sensor-rich human–machine interfaces. At the booth we see gesture-controlled dexterous hands and humanoid robots driven by embedded vision models on RZ MPUs with the DRP-AI accelerator, able to recognize finger counts, interpret hand poses and classify scenes such as humans, dogs or crowded spaces for context-aware behavior. More details on Renesas vision AI can be found at https://www.renesas.com/en/key-technologies/artificial-intelligence/ai-vision
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Recorded at Embedded World North America in Anaheim, the conversation highlights how Renesas scales the same RZ/V vision AI platform from compact camera modules to full robotic systems, combining convolutional-neural-network inference with classic computer-vision preprocessing on chip. This enables responsive gesture HMI, people and object detection, and crowd-density estimation at the edge without active cooling, in line with recent RZ/V2H and RZ/V2N devices that pair DRP-AI3 engines with quad Arm Cortex-A55 application processors for demanding workloads in deployment.
The tour then shifts to the sensor portfolio, starting from impedance-based touch sensing embedded into a robotic lawn-mower style handlebar that can detect one or two hands, slider-type interactions and virtual buttons for speed or mode control. Under the hood, Renesas capacitive and impedance sensors measure tiny changes in capacitance and electrical impedance to implement robust touch and touchless HMIs, a technique the company also showcases in hand-on and steering-wheel detection demos for automotive safety and industrial controls, all running on a unified platform.
Finally, walks through inductive position-sensing demos that show how PCB-coil-based, magnet-free IPS-class sensors track the relative position of metal targets and motor shafts inside a robotic joint, providing high-resolution angle feedback with strong immunity to stray magnetic fields compared with traditional resolvers. Combined with compact impedance-sensing evaluation boards and the broader Renesas embedded hardware–software ecosystem, these building blocks give engineers a coherent path from evaluation kits to production-grade humanoid robotics and collaborative automation hardware.
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