Resources

Every serving a Chef robot makes generates data that improves the next serving. We call this the data flywheel. With more than 120 million meals in production, Chef Robotics has built the world’s largest dataset of real food manipulation, and every production run widens that lead.

Food assembly robots provide a labor-equivalent solution for processes that have traditionally been difficult to automate. They also enable food manufacturers to reduce carbon emissions. Here’s how.

Chef’s AI-enabled robots deliver ROI for food manufacturers through five levers: lower labor costs, reduced food waste, higher throughput, and improved safety, all through a robotics-as-a-service (RaaS) model.

A new survey paper by Chef Robotics Senior Staff AI Research Scientist Inkyu Sa maps the relatively new field of world models and world-action models and evaluates which modeling approaches are best suited for robotics, autonomous driving, reinforcement learning, games, and general-purpose simulation.

A new survey paper by Chef Robotics Senior Staff AI Research Scientist Inkyu Sa examines the rapidly evolving field through one of robotics' hardest challenges: bimanual manipulation, or coordinating two arms to complete tasks such as folding fabric, assembling objects, and handling deformable materials.

We built ValueFormer, a critic that watches the cameras our policy uses and provides feedback on policy performance that behavior cloning can’t provide. Folded back into policy training, ValueFormer shows early on-robot success: two different critic-derived weightings each increased successful burger assembly from 70% to 85%.