  Cooking robots can enhance the home experience by reducing the burden of
daily chores. However, these robots must perform their tasks dexterously and
safely in shared human environments, especially when handling dangerous tools
such as kitchen knives. This study focuses on enabling a robot to autonomously
and safely learn food-cutting tasks. More specifically, our goal is to enable a
collaborative robot or industrial robot arm to perform food-slicing tasks by
adapting to varying material properties using compliance control. Our approach
involves using Reinforcement Learning (RL) to train a robot to compliantly
manipulate a knife, by reducing the contact forces exerted by the food items
and by the cutting board. However, training the robot in the real world can be
inefficient, and dangerous, and result in a lot of food waste. Therefore, we
proposed SliceIt!, a framework for safely and efficiently learning robot
food-slicing tasks in simulation. Following a real2sim2real approach, our
framework consists of collecting a few real food slicing data, calibrating our
dual simulation environment (a high-fidelity cutting simulator and a robotic
simulator), learning compliant control policies on the calibrated simulation
environment, and finally, deploying the policies on the real robot.
