Chef Robotics Builds Food-Manipulation Robots on NVIDIA Isaac Stack

Chef Robotics announced on October 7, 2026, that it is building its food-manipulation robotic systems on NVIDIA’s robotics development stack, adopting NVIDIA Isaac Sim, which is built on NVIDIA Omniverse libraries, for simulation and digital-twin work, and NVIDIA cuMotion for GPU-accelerated motion planning.

The company, which describes itself as the first to commercialize a scalable physical AI food robotics solution, said the stack is intended to accelerate how it develops and deploys its robots while maintaining reliability in production. Chef Robotics also reported that customer deployments of its systems have achieved up to 60% higher labor productivity.

According to the announcement, food ranks among the most demanding manipulation environments in the physical world: ingredients are deformable, wet, and highly variable, production lines run at high throughput in cold, harsh conditions, and food manufacturing faces a chronic labor shortage. Engineering robotic systems that perform reliably under those conditions, and getting new deployments running quickly, requires industrial-grade simulation and high-performance motion planning, the company said.

Simulation and Digital Twins With Isaac Sim

Chef Robotics uses the open Isaac Sim framework to construct physically accurate digital twins of its robotic work cells and of customer production lines. New configurations are designed, simulated, and validated in a virtual environment before deployment, allowing the company to stress-test cells against the variety of ingredients, containers, utensils, and line layouts found across its customers’ facilities. The company said this approach shortens the path from design to a working deployment on the plant floor.

NVIDIA describes Isaac Sim as an open-source reference framework built on Omniverse libraries for robotics simulation, testing, and synthetic data generation in physically based virtual environments. Per NVIDIA’s developer documentation, the framework ingests computer-aided design files, Unified Robot Description Format models, or real-world captures and converts them into USD, after which developers 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, and it 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, so developers can build custom OpenUSD-based simulators or integrate its capabilities into existing testing and validation pipelines. Isaac Sim is free to use, licensed as open source under Apache 2.0 and available on GitHub, according to NVIDIA.

Motion Planning With cuMotion

For motion planning, Chef Robotics uses cuMotion to generate fast, collision-free trajectories for robots working in cluttered, high-mix food production environments. The company said this allows its systems to plan and adapt motion efficiently across the range of tasks its robots handle in production, from piece-picking and multi-deposit assembly to coordinated multi-robot lines.

NVIDIA describes cuMotion as a high-performance motion generation library for robotics, focused mainly on manipulation, with GPU acceleration applied throughout. Its documented capabilities include kinematics and inverse kinematics, collision-aware inverse kinematics, collision-aware graph-based path planning, collision-aware trajectory optimization and end-to-end motion generation, reactive control through the RMPflow mathematical framework, time-optimal trajectory generation for paths in configuration or task space, collision sphere generation that approximates a closed mesh with a set of spheres, and robot segmentation that removes a robot arm’s contribution from depth image streams. Robots with any number of degrees of freedom are supported, and the library is implemented in C++ with a complete set of Python bindings.

According to the project’s repository, cuMotion descends from two earlier NVIDIA libraries: Lula, which was released for years as part of Isaac Sim and has been entirely subsumed, and cuRobo, a library developed by NVIDIA Research whose algorithms for collision-aware inverse kinematics and trajectory optimization appear in cuMotion in optimized and hardened implementations. The repository also states that cuMotion will soon serve as the motion generation backend for Isaac ROS cuMotion and the manipulation reference workflows in Isaac ROS. The current release is provided for Linux and Windows on x86-based computers running an NVIDIA GPU 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 part of how the company scales physical AI across the food industry.