When Cleaning Becomes Autonomous

Cleaning is a constant, repetitive need in any commercial environment—from hotel hallways to office buildings. It’s also one of the industries most affected by labor shortages and rising costs. Enter Peanut, a robot designed by California-based startup Peanut Robotics, which approaches cleaning not as a chore, but as a system that can learn and evolve.

With adaptive AI, autonomous navigation, and over 13,000 operational hours behind it, Peanut represents a shift: not just replacing labor, but redefining how it works.

Learning by Doing

Peanut runs on a combination of transformer-based AI and large language models—systems typically associated with text generation or voice interfaces. But here, they’re used to guide motion, respond to new environments, and optimize tasks like vacuuming and sweeping over time. Each site it operates in contributes data, training the robot to improve its technique and adapt to layout changes or different surfaces.

From Shortage to Solution

The global commercial cleaning industry is valued at over $300 billion and projected to double within the next decade. But it faces an ongoing workforce challenge. Peanut Robotics positions its product as a response: an automated cleaner that can be integrated into existing operations, covering routine tasks and reducing dependency on manual labor.

It’s not about replacing every role—but about reallocating human attention to tasks that require more judgment, oversight, or interaction.

Deployed and Tested

Peanut has been deployed across more than 25 different sites, logging thousands of hours of real-world cleaning time. These aren't lab tests—they're active environments like hotels and office spaces, where the robot has to work around furniture, people, and unpredictable conditions.

Every hour logged feeds back into the system, improving its speed, accuracy, and efficiency.