Researchers at the University of Cambridge in England trained a robot chef to taste foods at different stages of the chewing process to determine if they were seasoned enough.
Working with appliance maker Beko, researchers at the University of Cambridge trained their kitchen robot to evaluate the saltiness of a dish at different stages of the chewing process, mimicking a similar process in humans. Their findings could be useful in developing automated or semi-automated food preparation, helping robots learn what tastes good and what doesn’t, making them better cooks. When we chew food, we notice a change in its texture and taste. For example, when we bite into a fresh tomato in the heat of summer, we release juice, but when we chew, releasing both saliva and digestive enzymes, our perception of the taste of a tomato changes.
Taste cards for proven dishes
The robot chef, who had previously been trained to make omelettes based on feedback from human tasters, tasted nine variations of a simple scrambled egg and tomato dish at three different stages of the chewing process and produced “taste maps” of the various dishes. . The researchers found that this “taste on the go” approach significantly improved the robot’s ability to quickly and accurately judge the degree of saltiness in a dish compared to other electronic tasting technologies that only test a single homogenized sample. The results are published in Frontiers in Robotics & AI. In humans, the perception of taste is a complex process that has evolved over millions of years: the appearance, smell, texture, and temperature of food affect our perception of taste; saliva, formed during chewing, helps to transport chemical compounds from food to taste buds, located mainly on the tongue; signals from taste buds are transmitted to the brain. Once our brain is aware of the taste, we decide whether we like the food or not.
Taste: a very individual concept
The taste is also very individual: someone likes spicy dishes, and someone has a “sweet tooth”. A good cook, whether amateur or professional, relies on his sense of taste and can balance the various flavors of a dish to achieve a well-balanced end product. “Most home cooks are familiar with the concept of tasting on the go, which is testing a dish throughout the cooking process to make sure the flavors are well balanced,” says Grzegorz Sochacki, Cambridge School of Engineering, first author of the article. “If robots are to be used for any aspect of food preparation, it is important that they can ‘taste’ what they are cooking.” “When we taste, the chewing process also provides constant feedback to our brain,” said Dr. Arsen Abdulali, also from the engineering department. “Existing electronic testing methods only take one picture of a homogenized sample, so we wanted to replicate a more realistic chewing and tasting process in a robotic system, which should result in a tastier end product.”
Division of the Cambridge Bioinspired Robotics Laboratory
The researchers are members of the Cambridge Laboratory for Bioinspired Robotics, headed by Professor Fumiya Iida of the Faculty of Engineering, which trains robots to solve so-called “last mile” problems, which people consider simple but difficult robots. Cooking is one such challenge: the first tests done with their robot chef resulted in a tolerable omelette thanks to the comments of human tasters. “We needed something cheap, small and fast to add to our robot so it could do a tasting: it had to be cheap enough to use in the kitchen, small enough for the robot and fast enough to use while cooking.” Sokhatsky said. . To mimic the chewing and tasting process of a human in a food processor, the researchers attached a conductivity sensor, which acts as a salt sensor, to the robot’s arm. They cooked scrambled eggs and tomatoes, varying the number of tomatoes and the amount of salt in each dish. With the help of a probe, the robot “tried” the dishes like a grid, returning readings in just a few seconds. To mimic the texture change caused by chewing, the team then put the egg mixture into a blender and asked the robot to test the dish again. Different statements made at different times of chewing made it possible to map the taste of each dish. The results showed a significant improvement in the robots’ ability to judge salinity compared to other electronic tasting methods, which are often time consuming and produce only one reading. Although their method is a “proof of concept” (POC), the researchers say that by mimicking human chewing and tasting processes, robots could one day produce foods that people will love and that can be tailored to each person’s tastes. . “When a robot learns to cook, like any other chef, it needs guidance on how well it does it,” Dr. Abdulali said. “We want robots to understand the concept of taste, which would make them better cooks. In our experience, the robot can “see” the difference between food when chewing, which improves its palatability.” “Beko has a vision to bring robots into the home environment that are safe and easy to use,” said Dr. Muhammad V. Chugtai, chief scientist at Beko plc. “We believe that the development of robotic chefs will play an important role in residential and nursing homes in the future. This result is a leap forward in robotic cooking, and chewing using machine learning and deep learning algorithms will help robot chefs tailor taste to different dishes and users.” In the future, the researchers want to improve the food processor so it can taste different types of food and improve its sensory capabilities so it can taste sugary or fatty foods, for example. The study was supported in part by Beko plc and the Agri-Food Robotics Doctoral Training Center (Agriforwards CDT) of the Engineering and Physical Sciences Research Council (EPSRC). EPSRC is part of UK Research and Innovation (UKRI). Fumiya Iida is a member of Corpus Christi College, Cambridge.
Recommendations: Grzegorz Sochacki, Arsène Abdoulaly and Fumia Iida Frontières | Classification based on chewing-enhanced taste of multicomponent dishes for robotic cooking | Robotics and AI (frontiersin.org) “Frontiers of Robotics and AI” (2022). DOI: 10.3389/frobt.2022.886074