STMicroelectronics, a leading global semiconductor company, announced it is speeding up the worldwide adoption of physical AI systems, including humanoid, industrial, service, and healthcare robots. The company is incorporating its advanced robotics components into the NVIDIA Holoscan Sensor Bridge (HSB) reference platform. At the same time, high-accuracy NVIDIA Isaac Sim models of ST components are being added to both companies’ robotics ecosystems to enable faster, more precise sim-to-real development. Available today for developers are ST-enabled Leopard depth cameras integrated with NVIDIA HSB and high-fidelity ST IMU models in the Isaac Sim environment.
“ST is well engaged within the robotics community, providing robust support and a well-established ecosystem,” said Rino Peruzzi, Executive Vice President, Sales & Marketing, Americas & Global Key Account Organization at STMicroelectronics. “Our collaboration with NVIDIA aims to unleash the next wave of cutting-edge robotics innovation with developer and customer experience streamlined at every step, from the inception of AI algorithms to the seamless integration of sensors and actuators. This will accelerate the evolution of sophisticated AI-driven physical platforms.”
“Accelerating the development of next-generation autonomous systems requires high-fidelity simulation and seamless hardware integration to bridge the gap between virtual training and real-world deployment,” said Deepu Talla, Vice President of Robotics and Edge AI at NVIDIA. “The integration of STMicroelectronics’ sensor and actuator technologies with NVIDIA Isaac Sim, Holoscan Sensor Bridge and Jetson platforms provides developers with a unified foundation to build, simulate and deploy physical AI at scale.”
Simplifying sensor and actuator integration with the Holoscan Sensor Bridge
With the NVIDIA HSB, developers can unify, standardize, synchronize, and streamline data acquisition and logging from multiple ST sensors and actuators, a critical foundation for building high-fidelity NVIDIA Isaac models, accelerating learning, and minimizing the sim-to-real gap.
The goal is to simplify the process of connecting ST sensors and actuators to NVIDIA Jetson platforms through pre-integrated solutions for the combination of STM32 MCUs, advanced sensors (including IMUs, imagers, and ToF devices) and motor‑control solutions, particularly for humanoid robot designs. Leopard Imaging’s stereo depth camera for robots is the perfect example. Using ST imaging, depth and motion-sensing technologies, it is expected to support a broad wave of designs across Physical AI OEMs, academic research groups and the industrial robotics community.
Reducing cost, complexity challenges with high-fidelity modeling for Omniverse Isaac
Advanced robotics developers face high development costs, in addition to modeling challenges. High‑fidelity simulations with extensive randomization demand substantial GPU and CPU resources and large datasets. Selecting which parameters to randomize, and over what ranges, requires deep domain expertise. Poor choices can result in unrealistic scenarios or inefficient training. Finally, excessive variability can confuse models, slow convergence, and degrade real‑world performance when randomization no longer reflects plausible conditions.
ST and NVIDIA’s objective is to provide accurate, hardware-calibrated models for the comprehensive portfolio of ST components matching the requirements of advanced robotics. Following the availability of the first model of an IMU, ST is working to bring developers models of ToF sensors, actuators and other ICs derived from benchmark data collected on real ST hardware, using ST tools to capture accurate parameters and realistic behavior, resulting in models optimized to NVIDIA’s Isaac Sim ecosystem. NVIDIA HSB is being integrated into ST’s toolchain collaboratively.
As a result, ST and NVIDIA envision that more accurate models will significantly improve robot learning. With models that closely mirror real-world device behavior, robots can learn from simulations that better reflect actual conditions, shortening training cycles and lowering the cost of building and refining humanoid robotics applications.
More information on NVIDIA Holoscan Sensor Bridge (HSB) is accessible here.
More information on ST solutions to accelerate physical AI development with NVIDIA is accessible here.
