A ‘chef’ robot has been trained by researchers at the University of Cambridge to assess the saltiness of a dish at different stages of the chewing process, mimicking a similar process in humans.
They think the findings could be useful in the development of automated or semi-automated food preparation by helping robots learn what tastes good and what doesn’t.
The robot chef, who was previously trained to make omelets based on feedback from human tasters, tasted nine different variations of a simple dish of scrambled eggs and tomatoes at three different stages of the chewing process, and produces “taste maps” of the different dishes.
The researchers found that this “taste as you go” approach significantly improved the robot’s ability to quickly and accurately assess the saltiness of the dish compared to other electronic tasting technologies, which only test a single sample. homogenized.
“Most home cooks are familiar with the concept of tasting as you go — checking a dish through the cooking process to see if the balance of flavors is right,” said Grzegorz Sochacki, the first author of the item. “If robots are to be used for certain aspects of food preparation, it is important that they are able to ‘taste’ what they are cooking.”
“When we taste, the chewing process also provides continuous feedback to our brain,” said co-author Dr. Arsen Abdulali. “Current electronic testing methods only take a single snapshot from 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. “
To mimic the human chewing and tasting process in their robot chef, the researchers attached a conductance probe, which acts as a salinity sensor, to a robot arm. They made scrambled eggs and tomatoes, varying the number of tomatoes and the amount of salt in each dish.
Using the probe, the robot “tasted” the dishes like a grid, returning a reading in just seconds.
To mimic the change in texture caused by chewing, the team then put the egg mixture in a blender and had the robot test the dish again. The different readings at different “chew” points produced taste maps of each dish.
Their results showed a significant improvement in the robots’ ability to assess salinity compared to other electronic tasting methods, which are often time-consuming and provide only a single reading.
Although their technique is a proof of concept, the researchers said that by mimicking human chewing and tasting processes, the robots will eventually be able to produce foods that humans will enjoy and could be modified to suit individual tastes.
“When a robot learns to cook, like any other cook, it needs feedback on its performance,” Abdulali said. “We want robots to understand the concept of taste, which will make them better cooks. In our experiment, the robot can “see” the difference in the food as it is chewed, which improves its ability to taste. »
Dr Muhammad W Chughtai, Principal Scientist at Beko, who is collaborating on the project, said: “We believe the development of robotic chefs will play a major role in busy households and assisted living facilities in the future. This result is a leap forward in robotic cooking, and using machine and deep learning algorithms, the mastication will help robot chefs adjust the taste of different dishes and users.
In the future, the researchers are looking to improve the robot chef so that it can taste different types of food and improve its sensing capabilities so that it can taste sweet or fatty foods, for example.
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