Chef Robotics is building its food-manipulation robotic systems on NVIDIA’s robotics development stack. The company announced on October 7, 2026, that it is adopting NVIDIA Isaac Sim for simulation and digital-twin work, and NVIDIA cuMotion for GPU-accelerated motion planning.
Chef Robotics describes itself as the first to commercialize a scalable physical AI food robotics solution. The company stated that the NVIDIA stack is intended to accelerate the development and deployment of its robots while maintaining reliability in production. Customer deployments of Chef Robotics’ systems have achieved up to 60% higher labor productivity.
Food manipulation presents significant challenges in the physical world. Ingredients are deformable and highly variable, production lines operate at high throughput in cold, harsh conditions, and the food manufacturing industry faces a chronic labor shortage. Engineering reliable robotic systems for these conditions and quickly deploying new systems requires industrial-grade simulation and high-performance motion planning.
Simulation and Digital Twins
Chef Robotics utilizes the open Isaac Sim framework to create physically accurate digital twins of its robotic work cells and customer production lines. New configurations are designed, simulated, and validated in a virtual environment before deployment. This allows the company to test cells against various ingredients, containers, utensils, and line layouts found across customer facilities. This approach aims to shorten the time from design to a working deployment on the plant floor.
NVIDIA describes Isaac Sim as an open-source framework built on Omniverse libraries for robotics simulation, testing, and synthetic data generation in physically based virtual environments. The framework can ingest computer-aided design files, Unified Robot Description Format models, or real-world captures and convert them into USD. Developers then assemble scenes by assigning materials, enabling physics, and configuring robot and sensor models. The framework supports controllable synthetic data generation, which can be augmented with NVIDIA’s Cosmos world foundation models. It also allows perception and mobility stacks to be trained in simulation and evaluated through software-in-the-loop or hardware-in-the-loop testing. NVIDIA states the framework is fully extensible, enabling developers to build custom OpenUSD-based simulators or integrate its capabilities into existing testing and validation pipelines. Isaac Sim is free to use and licensed as open source under Apache 2.0.
Motion Planning
For motion planning, Chef Robotics employs cuMotion to generate fast, collision-free trajectories for robots operating in cluttered, high-mix food production environments. This capability allows Chef Robotics’ systems to efficiently plan and adapt motion across various tasks, from piece-picking and multi-deposit assembly to coordinated multi-robot lines.
NVIDIA describes cuMotion as a high-performance motion generation library for robotics, primarily focused on manipulation, with GPU acceleration. Its capabilities include kinematics, collision-aware inverse kinematics, collision-aware graph-based path planning, collision-aware trajectory optimization, and end-to-end motion generation. It also offers reactive control through the RMPflow mathematical framework and time-optimal trajectory generation. The library supports robots with any number of degrees of freedom and is implemented in C++ with Python bindings. cuMotion descends from earlier NVIDIA libraries, Lula and cuRobo. The current release is available for Linux and Windows on x86-based computers with NVIDIA GPUs of the Turing generation or later, and for Jetson Orin, Jetson Thor, and DGX Spark.
“Food is one of the hardest manipulation problems in the physical world, and solving it requires both world-class AI and world-class engineering tools,” said Rajat Bhageria, founder and CEO of Chef Robotics. “Building on NVIDIA’s robotics stack lets our team move faster, designing, simulating, and validating systems in a virtual environment, then deploying them with confidence into real production facilities.” Bhageria described the stack as a key component in how the company scales physical AI across the food industry.
Chef Robotics’ adoption of the NVIDIA Isaac stack aims to accelerate the development and deployment of its food-manipulation robots, a critical step in addressing labor shortages and operational challenges within the food manufacturing sector.
