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... Visual feedback was used to adjust frying time of a sausage, but only simple and not robust approach of background masking and averaging the hue was used Mauch et al. (2017). Teleoperation was also used to help the robotic chef at cake decoration Bolano et al. (2019). Loading dishwashers with robotic arms was also investigated Voysey et al. (2021). ...
Chefs frequently rely on their taste to assess the content and flavor of dishes during cooking. While tasting the food, the mastication process also provides continuous feedback by exposing the taste receptors to food at various stages of chewing. Since different ingredients of the dish undergo specific changes during chewing, the mastication helps to understand the food content. The current methods of electronic tasting, on the contrary, always use a single taste snapshot of a homogenized sample. We propose a robotic setup that uses the mixing to imitate mastication and tastes the dish at two different mastication phases. Each tasting is done using a conductance probe measuring conductance at multiple, spatially distributed points. This data is used to classify 9 varieties of scrambled eggs with tomatoes. We test four different tasting methods and analyze the resulting classification performance, showing a significant improvement over tasting homogenized samples. The experimental results show that tasting at two states of mechanical processing of the food increased classification F1 score to 0.93 in comparison to the traditional tasting of a homogenized sample resulting in F1 score of 0.55. We attribute this performance increase to the fact that different dishes are affected differently by the mixing process, and have different spatial distributions of the salinity. It helps the robot to distinguish between dishes of the same average salinity, but different content of ingredients. This work demonstrates that mastication plays an important role in robotic tasting and implementing it can improve the tasting ability of robotic chefs.
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.
Cooking soup is a complex dynamic process, where the properties and taste of ingredients change during long temperature exposure. Furthermore, the simmering process of a soup also causes evaporation of the water, which increases the salt density in a bouillon. To mitigate this problem, we developed a closed-loop robotic system that allows cooking soups based on salinity and pH sensing. By taking into account that both salinity and pH are subject to change during the cooking, we recorded the salinity and pH over a complete course of cooking by an expert human and employed a proportional controller that adds salt and water into the soup. For the evaluation, we employed the proposed approach to cook a tomato soup with three different initial conditions. The results suggest that the system reaches the target pH and salinity reasonably close, even for significantly different soup bases.KeywordsRobotic CookingTaste FeedbackClosed-loop Control
