Learn why food is physical AI's hardest problem at RoboBusiness

Chef Robotics has designed its robotic system to handle high-mix food preparation. | Source: Chef Robotics

Physical AI has made remarkable strides, but most of the time, we use rigid objects to train foundation models. Food represents one of the most complex manipulation challenges in robotics, noted Chef Robotics. Every ingredient is deformable, inconsistent in weight and texture, sensitive to temperature, and must be handled with calibrated force across thousands of variations.

Chef Robotics said it has built the largest real-world dataset of deformable material manipulation. The company has completed over 118 million servings in production across over a dozen food manufacturing facilities in North America and Europe. This data serves as the basis for its Food Foundation Model (FFM), which enables robots to generalize to new ingredients with minimal retraining.

At RoboBusiness 2026 on Oct. 20 and 21 in Santa Clara, Calif., Rajat Bhageria, the founder and CEO of Chef Robotics, will give the talk, “Why Food is Physical AI’s Hardest Problem and Most Promising Catalyst.”

Chef CEO to explore challenges, opportunities for food robotics

This session will explore what makes food such a demanding benchmark for physical AI. This includes:

The sensor-fusion challenges of grasping soft and unpredictable objects
The force-control precision required to handle fragile versus dense materials
Why real-world variation at scale is irreplaceable for training robust policies

More importantly, Bhageria will show why solving food unlocks progress far beyond this industry. The techniques developed at Chef Robotics — such as adaptive grasping, tactile feedback integration, and high-variance training distributions — may transfer to medical devices, flexible packaging, agriculture, and other domains involving deformable materials.

Attendees will leave with a concrete framework for thinking about deformable material manipulation as the next frontier in physical AI, and evidence that real-world data at scale is what separates lab demos from deployable systems.

San Francisco-based Chef Robotics is a physical AI company that automates food production. Previously, Bhageria was a founder and managing partner at Prototype Capital, a pre-seed venture capital fund investing in founders who apply new technology to old industries.

Before Prototype Capital, Bhageria founded ThirdEye, a company that developed assistive technology for the visually impaired. ThirdEye was ultimately acquired. Bhageria holds a master’s degree in robotics and machine learning and a bachelor’s degree in economics from the University of Pennsylvania.

Register now for RoboBusiness 2026

RoboBusiness 2026 is the leading event for commercial robotics developers. Attendees will gain the latest insights from experts in robotics and AI on cutting-edge research, industry trends, and innovative applications in sectors such as manufacturing, healthcare, agriculture, logistics, and more.

The show also features a number of networking opportunities to give attendees the chance to connect with other experts in the industry. This will include the Mix and Mingle reception on the first day of the show.

Buy your full conference pass and gain full access to all keynotes, technical sessions, networking receptions, and special events. Discounts are also available for academia, associations, and corporate groups. Please email events[at]arrowfly.com for more details about our discount programs.

For information about sponsorship and exhibition opportunities, download the prospectus. Questions regarding sponsorship opportunities should be directed to Colleen Sepich at csepich[AT]arrowfly.com.