Chef Robotics CEO to Explain Why Food Is Physical AI's Hardest Problem

Physical AI has advanced significantly, yet foundation models are typically trained using rigid objects. According to Chef Robotics, food represents one of the most complex manipulation challenges in robotics. Every ingredient is deformable, varies in weight and texture, is sensitive to temperature, and requires calibrated force across thousands of variations.

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

At RoboBusiness 2026, taking place October 20–21 in Santa Clara, California, Rajat Bhageria, founder and CEO of Chef Robotics, will deliver a talk titled 'Why Food is Physical AI’s Hardest Problem and Most Promising Catalyst.' The session will address what makes food such a demanding benchmark for physical AI, including sensor-fusion challenges of grasping soft and unpredictable objects, the force-control precision required for delicate versus dense materials, and why large-scale real-world variation is indispensable for training robust policies.

Bhageria will further explain why solving food-related challenges can accelerate progress in other industries. Techniques developed by 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 gain a concrete framework for viewing deformable material manipulation as the next frontier in physical AI, supported by evidence that real-world data at scale differentiates lab demos from deployable systems.

Chef Robotics, headquartered in San Francisco, is a physical AI company automating food production. Previously, Bhageria founded Prototype Capital, a pre-seed venture fund focusing on founders applying new technology to traditional industries, and later ThirdEye, which developed assistive technology for the visually impaired and was eventually acquired. He holds a master's degree in robotics and machine learning and a bachelor's degree in economics from the University of Pennsylvania.

RoboBusiness 2026 is a premier event for commercial robotics developers, offering insights into cutting-edge research, industry trends, and innovative applications in manufacturing, healthcare, agriculture, logistics, and more. The event also provides networking opportunities, including the Mix and Mingle reception on the first day. A full conference pass grants access to keynotes, technical sessions, networking receptions, and special events. Discounts are available for academia, associations, and corporate groups. For sponsorship and exhibition details, interested parties can download the prospectus or contact Colleen Sepich at [email protected].

Early registration for RoboBusiness 2026 offers discounted rates. The conference pass provides entry to all sessions and networking activities.