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Sensorized Compliant Robot Gripper for Estimating the Cooking Time of Boil-Cooked Vegetables

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 412)


Many cooking tasks rely on physically interacting with and sensing soft objects. One widely performed assessment is identifying when a vegetable is cooked. Commonly, we may interact with the food item and use tactile feedback to estimate if the food is cooked or not. This is also a task performed at scale in food supply chains. To address this, we have developed a general-purpose model for modelling the kinetics and thermal properties of vegetable cooking. We show that by identifying the size, and stiffness of the vegetable at two points in the cooking process the time for the vegetable to cook can be identified. With this in mind, we have developed a compliant tactile testing device that includes a tactile force sensor that can be used for measuring stiffness, and a proprioceptive sensing method which can be used to measure the size. The mechanism is robust and high torque, in addition to being simple and low cost in terms of fabrication. Using this model and device we demonstrate the accuracy in predicting the cooking time for potatoes of various sizes, and benchmark this in comparison to when used a fixed cooking time. We demonstrate that the model-based approach significantly improves the estimation and outcome of the cooking process. Whilst we demonstrate this approach on potatoes, the hardware, and model to other vegetable cooking processes.


  • Robotic kitchen
  • Tactile sensing
  • Flexible grippers
  • Food modelling

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  • DOI: 10.1007/978-3-030-95892-3_17
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We are grateful for the support from Beko plc and Engineering and Physical Sciences Research Council (EPSRC) Agriforwards CDT Project [EP/S023917/1] who made this work possible.

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Correspondence to Grzegorz Sochacki .

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Sochacki, G., Hughes, J., Iida, F. (2022). Sensorized Compliant Robot Gripper for Estimating the Cooking Time of Boil-Cooked Vegetables. In: Ang Jr, M.H., Asama, H., Lin, W., Foong, S. (eds) Intelligent Autonomous Systems 16. IAS 2021. Lecture Notes in Networks and Systems, vol 412. Springer, Cham.

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