Planning Framework for Robotic Pizza Dough Stretching with a Rolling Pin

Kim, Jung‐Tae; Ruggiero, Fabio; Lippiello, Vincenzo; Siciliano, Bruno

doi:10.1007/978-3-030-93290-9_9

Cited by 5 publications

(8 citation statements)

References 31 publications

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“…Moreover, the analytical dynamics model is tailored to the rolling primitive, which makes it unusable for any other action parameterization or tasks. Recent works [2,5,13,18,23] also use action primitives for their tasks but learn a dynamics model in a data-driven fashion to improve generalization. However, many rely heavily on feature engineering for the state of the dough: Kim et al [13] uses the set of distances between the boundary points and the center of the dough, Matl et al [23] uses a bounding box for the dough, and Calinon et al and Figueroa et al [2,5] fit an ellipse on the 2D view of the dough.…”

Section: A Model-based Approaches For Dough Manipulationmentioning

confidence: 99%

“…Recent works [2,5,13,18,23] also use action primitives for their tasks but learn a dynamics model in a data-driven fashion to improve generalization. However, many rely heavily on feature engineering for the state of the dough: Kim et al [13] uses the set of distances between the boundary points and the center of the dough, Matl et al [23] uses a bounding box for the dough, and Calinon et al and Figueroa et al [2,5] fit an ellipse on the 2D view of the dough. These manually defined state spaces have limited representation power and cannot easily transfer to other tasks.…”

Section: A Model-based Approaches For Dough Manipulationmentioning

confidence: 99%

“…D EFORMABLE object manipulation allows autonomous agents to expand their applicability in our daily lives. Many household tasks such as folding laundry, cleaning rooms, and cooking food require a substantial amount of interaction with deformable objects, and recent works [6,9,13,20,35,36] have shown great progress towards building a household robot. However, there are many challenges within deformable object manipulation that are yet to be solved.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, deformable objects have high degrees of freedom, making them hard to model. Many prior works rely on hand-designed representations for deformable object manipulation [5,13,23], which results in task-specific representations that lack generalizability. Recent works design more general data-driven methods Fig.…”

Section: Introductionmentioning

confidence: 99%

“…Manipulation of elastoplastic objects like dough and clay has wide application for cooking and art-making, and there exists a rich list of literature on food manipulation [1,5,19,34,35]. Similar to other deformable objects, prior works have proposed action primitives for dough flattening [13,23,34]. They parameterize the action space by a few parameters such as the direction and length of a rolling trajectory, which limits the flexibility and efficiency of the obtained trajectory.…”

Section: Introductionmentioning

confidence: 99%

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Learning Closed-Loop Dough Manipulation Using a Differentiable Reset Module

Lin

Held

2022

IEEE Robot. Autom. Lett.

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Deformable object manipulation has many applications such as cooking and laundry folding in our daily lives. Manipulating elastoplastic objects such as dough is particularly challenging because dough lacks a compact state representation and requires contact-rich interactions. We consider the task of flattening a piece of dough into a specific shape from RGB-D images. While the task is seemingly intuitive for humans, there exist local optima for common approaches such as naive trajectory optimization. We propose a novel trajectory optimizer that optimizes through a differentiable "reset" module, transforming a single-stage, fixed-initialization trajectory into a multistage, multi-initialization trajectory where all stages are optimized jointly. We then train a closed-loop policy on the demonstrations generated by our trajectory optimizer. Our policy receives partial point clouds as input, allowing ease of transfer from simulation to the real world. We show that our policy can perform real-world dough manipulation, flattening a ball of dough into a target shape. Supplementary videos can be found on our project website: https://sites.google.com/view/dough-manipulation.

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Section: A Model-based Approaches For Dough Manipulationmentioning

confidence: 99%

Section: A Model-based Approaches For Dough Manipulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning Closed-Loop Dough Manipulation Using a Differentiable Reset Module

Lin

Held

2022

IEEE Robot. Autom. Lett.

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Towards practical robotic chef: Review of relevant work and future challenges

Sochacki,

Zhang,

Abdulali

et al. 2024

Journal of Field Robotics

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Robotic chefs are a promising technology that can improve the availability of quality food by reducing the time required for cooking, therefore decreasing food's overall cost. This paper clarifies and structures design and benchmarking rules in this new area of research, and provides a comprehensive review of technologies suitable for the construction of cooking robots. The diner is an ultimate judge of the cooking outcome, therefore we put focus on explaining human food preferences and perception of taste and ways to use them for control. Mechanical design of robotic chefs at a practically low cost remains the challenge, but some recently published gripper designs as well as whole robotic systems show the use of cheap materials or off‐the‐shelf components. Moreover, technologies like taste sensing, machine learning, and computer vision are making their way into robotic cooking enabling smart sensing and therefore improving controllability and autonomy. Furthermore, objective assessment of taste and food palatability is a challenge even for trained humans, therefore the paper provides a list of procedures for benchmarking the robot's tasting and cooking abilities. The paper is written from the point of view of a researcher or engineer building a practical robotic system, therefore there is a strong priority for solutions and technologies that are proven, robust and self‐contained enough to be a part of a larger system.

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