Robot 'chef' learns to recreate recipes from watching food videos

Date:
June 5, 2023
Source:
University of Cambridge
Summary:
Researchers have trained a robotic 'chef' to watch and learn from
cooking videos, and recreate the dish itself.


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==========================================================================
FULL STORY ========================================================================== Researchers have trained a robotic 'chef' to watch and learn from cooking videos, and recreate the dish itself.

The researchers, from the University of Cambridge, programmed their
robotic chef with a 'cookbook' of eight simple salad recipes. After
watching a video of a human demonstrating one of the recipes, the robot
was able to identify which recipe was being prepared and make it.

In addition, the videos helped the robot incrementally add to its
cookbook. At the end of the experiment, the robot came up with a ninth
recipe on its own.

Their results, reported in the journal IEEE Access, demonstrate how video content can be a valuable and rich source of data for automated food production, and could enable easier and cheaper deployment of robot chefs.

Robotic chefs have been featured in science fiction for decades, but in reality, cooking is a challenging problem for a robot. Several commercial companies have built prototype robot chefs, although none of these are currently commercially available, and they lag well behind their human counterparts in terms of skill.

Human cooks can learn new recipes through observation, whether
that's watching another person cook or watching a video on YouTube,
but programming a robot to make a range of dishes is costly and
time-consuming.

"We wanted to see whether we could train a robot chef to learn in the
same incremental way that humans can -- by identifying the ingredients
and how they go together in the dish," said Grzegorz Sochacki from
Cambridge's Department of Engineering, the paper's first author.

Sochacki, a PhD candidate in Professor Fumiya Iida's Bio-Inspired
Robotics Laboratory, and his colleagues devised eight simple salad
recipes and filmed themselves making them. They then used a publicly
available neural network to train their robot chef. The neural network
had already been programmed to identify a range of different objects,
including the fruits and vegetables used in the eight salad recipes
(broccoli, carrot, apple, banana and orange).

Using computer vision techniques, the robot analysed each frame of video
and was able to identify the different objects and features, such as a
knife and the ingredients, as well as the human demonstrator's arms, hands
and face. Both the recipes and the videos were converted to vectors and
the robot performed mathematical operations on the vectors to determine
the similarity between a demonstration and a vector.

By correctly identifying the ingredients and the actions of the
human chef, the robot could determine which of the recipes was being
prepared. The robot could infer that if the human demonstrator was
holding a knife in one hand and a carrot in the other, the carrot would
then get chopped up.

Of the 16 videos it watched, the robot recognised the correct recipe
93% of the time, even though it only detected 83% of the human chef's
actions. The robot was also able to detect that slight variations in
a recipe, such as making a double portion or normal human error, were variations and not a new recipe. The robot also correctly recognised the demonstration of a new, ninth salad, added it to its cookbook and made it.

"It's amazing how much nuance the robot was able to detect," said
Sochacki.

"These recipes aren't complex -- they're essentially chopped fruits and vegetables, but it was really effective at recognising, for example,
that two chopped apples and two chopped carrots is the same recipe as
three chopped apples and three chopped carrots." The videos used to
train the robot chef are not like the food videos made by some social
media influencers, which are full of fast cuts and visual effects, and
quickly move back and forth between the person preparing the food and the
dish they're preparing. For example, the robot would struggle to identify
a carrot if the human demonstrator had their hand wrapped around it --
for the robot to identify the carrot, the human demonstrator had to hold
up the carrot so that the robot could see the whole vegetable.

"Our robot isn't interested in the sorts of food videos that go viral on
social media -- they're simply too hard to follow," said Sochacki. "But
as these robot chefs get better and faster at identifying ingredients
in food videos, they might be able to use sites like YouTube to learn
a whole range of recipes." The research was supported in part by Beko
plc and the Engineering and Physical Sciences Research Council (EPSRC),
part of UK Research and Innovation (UKRI).

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========================================================================== Story Source: Materials provided by University_of_Cambridge. The original
text of this story is licensed under a Creative_Commons_License. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Grzegorz Sochacki, Arsen Abdulali, Narges Khadem Hosseini,
Fumiya Iida.

Recognition of Human Chef's Intentions for Incremental Learning of
Cookbook by Robotic Salad Chef. IEEE Access, 2023; 1 DOI: 10.1109/
ACCESS.2023.3276234 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/06/230605181344.htm

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