Robotic chefs are a promising technology that can improve the availability of quality food by reducing the time required for cooking, therefore decreasing food's overall cost. This paper clarifies and structures design and benchmarking rules in this new area of research, and provides a comprehensive review of technologies suitable for the construction of cooking robots. The diner is an ultimate judge of the cooking outcome, therefore we put focus on explaining human food preferences and perception of taste and ways to use them for control. Mechanical design of robotic chefs at a practically low cost remains the challenge, but some recently published gripper designs as well as whole robotic systems show the use of cheap materials or off-the-shelf components. Moreover, technologies like taste sensing, machine learning and computer vision are making their way into robotic cooking enabling smart sensing and therefore improving controllability and autonomy. Furthermore, objective assessment of taste and food palatability is a challenge even for trained humans, therefore the paper provides a list of procedures for benchmarking the robot's tasting and cooking abilities. The paper is written from the point of view of a researcher or engineer building a practical robotic system, therefore there is a strong priority for solutions and technologies that are proven, robust and self-contained enough to be a part of a larger system.