Robotic Dough Shaping
Jan Ondras, Di Ni, Xi Deng, Zeqi Gu, Henry Zheng, and Tapomayukh, Bhattacharjee (Cornell University)

TL;DR
This paper presents robotic methods for shaping dough-like deformable objects into specific 2D shapes using a robotic arm, sensors, and various control policies, achieving high accuracy across different materials and sizes.
Contribution
It introduces and compares control policies for dough shaping with a robotic arm, including a novel dough shrinking action, and evaluates their effectiveness across multiple materials and target shapes.
Findings
Rolling from the highest dough point is more efficient.
Stopping at the current boundary can be better than reaching the target outline.
Proper tuning of shrink and expand actions improves shaping performance.
Abstract
Robotic manipulation of deformable objects gains great attention due to its wide applications including medical surgery, home assistance, and automatic food preparation. The ability to deform soft objects remains a great challenge for robots due to difficulties in defining the problem mathematically. In this paper, we address the problem of shaping a piece of dough-like deformable material into a 2D target shape presented upfront. We use a 6 degree-of-freedom WidowX-250 Robot Arm equipped with a rolling pin and information collected from an RGB-D camera and a tactile sensor. We present and compare several control policies, including a dough shrinking action, in extensive experiments across three kinds of deformable materials and across three target dough shape sizes, achieving the intersection over union (IoU) of 0.90. Our results show that: i) rolling dough from the highest dough point…
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Taxonomy
TopicsSoft Robotics and Applications · Advanced Materials and Mechanics · Interactive and Immersive Displays
