Closed-Loop Robotic Cooking of Scrambled Eggs with a Salinity-based ‘Taste’ Sensor

Sochacki, Grzegorz; Hughes, Josie; Hauser, Simon; Iida, Fumiya

doi:10.1109/iros51168.2021.9636750

Cited by 11 publications

(13 citation statements)

References 15 publications

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“…Every tasting model needs a computation component to produce a meaningful signal Di Rosa et al (2020) , otherwise, it remains a collection of measurements. In our application, we use previously established taste metrics Sochacki et al (2021) to reduce the dimensionality of data. These taste metrics relate to the average salinity and “mixed-ness” of a dish.…”

Section: Model Of Tastingmentioning

confidence: 99%

“…For the need of this experiment, we created a salinity sensor probe made out of a testing tip of a standard salinity sensor testing device (ExTech salinity probe) same as in our previous work Sochacki et al (2021) . The same calibration procedure was followed, as well as placing the sample on a non-conductive (ceramic) plate.…”

Section: Model Of Tastingmentioning

confidence: 99%

“…Loading dishwashers with robotic arms was also investigated Voysey et al (2021) . Some robotic chefs can improve their cooking based on a feedback from diners Junge et al (2020) or replicate a human cooked dish using its own taste Sochacki et al (2021) .…”

Section: Introductionmentioning

confidence: 99%

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Mastication-Enhanced Taste-Based Classification of Multi-Ingredient Dishes for Robotic Cooking

2022

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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.

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Section: Model Of Tastingmentioning

confidence: 99%

Section: Model Of Tastingmentioning

confidence: 99%

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Mastication-Enhanced Taste-Based Classification of Multi-Ingredient Dishes for Robotic Cooking

2022

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“…Nowadays, the development of intelligent environments is gaining strength, including smart homes [16]. Robotic technology has been included in these environments with different fronts such as people care [17], cleaning [18] and even cooking [19]. In [20] and [21], the development of assistive robots to address patient isolations by COVID is exposed, however, the development is oriented to systems telecommanded by cellular mobile equipment.…”

Section: Introductionmentioning

confidence: 99%

Deep learning speech recognition for residential assistant robot

Moreno

Estepa

2023

IJ-AI

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This work presents the design and validation of a voice assistant to command robotic tasks in a residential environment, as a support for people who require isolation or support due to body motor problems. The preprocessing of a database of 3600 audios of 8 different categories of words like “paper”, “glass” or “robot”, that allow to conform commands such as "carry paper" or "bring medicine", obtaining a matrix array of Mel frequencies and its derivatives, as inputs to a convolutional neural network that presents an accuracy of 96.9% in the discrimination of the categories. The command recognition tests involve recognizing groups of three words starting with "robot", for example, "robot bring glass", and allow identifying 8 different actions per voice command, with an accuracy of 88.75%.

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“…Various types of electronic tongues and similar sensors are capable of feats like classifying wine age [1], finding pork contamination in mutton [2] and assessing fish freshness [3]. Moreover, the electronic taste has been used as feedback for a robotic chef, controlling the saltiness and texture of scrambled eggs [4]. Some approaches also use human taste to adjust robotic chefs' cooking [5].…”

Section: Introductionmentioning

confidence: 99%

Theoretical Framework for Human-Like Robotic Taste with Reference to Nutritional Needs

Sochacki,

Abdulali,

Cheke

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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This paper contains a theoretical framework for an intelligent robot that can feed itself with organic matter and learns to like and want certain foods. Furthermore, being tied to the onboard generation of power, the robot’s learning is grounded to physical requirements that need to be met for continued robot operation. We posit a system wherein the electrical power is generated by a microbial fuel cell(MFC), and the food value can be assessed by measuring the current generated. Interestingly, the MFC requires feed similar in many parameters to food preferred by most people with respect to parameters like salinity or pH. This property is theorized to be a good start for teaching a robotic chef human preferences. We also propose a circuit that injects additional current, simulating social cues for some foods, and effectively resulting in learning an acquired taste.

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Closed-Loop Robotic Cooking of Scrambled Eggs with a Salinity-based ‘Taste’ Sensor

Cited by 11 publications

References 15 publications

Mastication-Enhanced Taste-Based Classification of Multi-Ingredient Dishes for Robotic Cooking

Mastication-Enhanced Taste-Based Classification of Multi-Ingredient Dishes for Robotic Cooking

Deep learning speech recognition for residential assistant robot

Theoretical Framework for Human-Like Robotic Taste with Reference to Nutritional Needs

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