A “chef” robot was trained to taste foods at different stages of the chewing process to assess whether they are seasoned enough.
Working in conjunction with home appliance maker Beko, researchers from the University of Cambridge trained their chef robot to assess the saltiness of a dish at different stages of the chewing process, mimicking a similar process in humans.
Their 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, which would make them better cooks.
When we chew our food, we notice a change in texture and taste. For example, biting into a fresh tomato at the height of summer will release juice, and as we chew, releasing both saliva and digestive enzymes, our perception of the tomato’s flavor will change.
The robot chef, who was previously trained to make omelettes based on human taster feedback, 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. .
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. The results are published in the journal Frontiers of robotics and AI.
Taste perception is a complex process in humans that has evolved over millions of years: the appearance, smell, texture and temperature of food affect how we perceive taste; saliva produced during chewing helps transport chemical compounds from food to taste receptors, primarily on the tongue; and signals from taste receptors are transmitted to the brain. Once our brain is aware of flavor, we decide whether or not we like food.
The taste is also very individual: some people like spicy food, while others have a sweet tooth. A good cook, whether amateur or professional, relies on their sense of taste and can balance the different flavors of a dish into a well-rounded end product.
“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 from the engineering department. of Cambridge, the first author of the article. “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, also from the Department of Engineering. “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. . »
The researchers are members of Cambridge’s Bio-Inspired Robotics Lab led by Professor Fumiya Iida from the Department of Engineering, which focuses on training robots to solve so-called last-meter problems that humans find easy, but robots find it difficult. Cooking is one such task: previous tests with their “chef” robot produced a passable omelet using feedback from human tasters.
“We needed something cheap, small and quick to add to our robot so it could do the tasting: it had to be cheap enough to use in a kitchen, small enough for a robot and fast enough to be used while cooking,” Sochacki said. .
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 say 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. »
“Beko’s vision is to bring robots into the home environment that are safe and easy to use,” said Dr. Muhammad W. Chughtai, Principal Scientist at Beko plc. “We believe the development of robotic chefs will play a major role in busy homes and assisted living facilities in the future. This result is a leap forward in robotic cooking, and using machine and deep learning algorithms, mastication will help robot chefs adapt. taste for 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.
The research was partly funded by Beko plc and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI). Fumiya Iida is a Fellow of Corpus Christi College, Cambridge.
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