Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey

By Jose Sanchez, Juan Antonio Corrales, Belhassen-Chedli Bouzgarrou, Y. Mezouar. 2018

474 citations recorded by the source corpus.

We present a survey of recent work on robot manipulation and sensing of deformable objects, a field with relevant applications in diverse industries such as medicine (e.g. surgical assistance), food handling, manufacturing, and domestic chores (e.g. folding clothes). We classify the reviewed approaches into four categories based on the type of object they manipulate. Furthermore, within this object classification, we divide the approaches based on the particular task they perform on the deformable object. Finally, we conclude this survey with a discussion of the current state-of-the-art approaches and propose future directions within the proposed classification.

Subfields: Aerospace Engineering, Biomedical Engineering, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Peter R. Florence
2. Jia Pan
3. Ken Goldberg
4. Katsu Yamane
5. David Held
6. Jens Kober
7. Erwin Coumans
8. John F. Canny

In Biomedical Engineering

1. Andy Zeng
2. Jia Pan
3. Ken Goldberg
4. David Held
5. Jens Kober
6. Erwin Coumans
7. Vikas Sindhwani
8. Brijen Thananjeyan

In Control and Systems Engineering

1. Sergey Levine
2. Andy Zeng
3. Peter R. Florence
4. Jonathan Tompson
5. Jake Olkin
6. Jia Pan
7. Ken Goldberg
8. Katsu Yamane

Citations in the corpus

• SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation (2020, background): “We refer to [26, 27] for a more detailed survey on prior methods for robot manipulation of deformable objects.”
• Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks (2020, methodology): “Much research on robotic fabric manipulation [10], [51], [13] uses bilateral robots and gravity to expose corners for ease of manipulation, which enables iteratively re-grasping the fabric’s lowest hanging point [48], [34], [35].”
• Deep Imitation Learning of Sequential Fabric Smoothing Policies (2019, background): “Well-known research on robotic fabric manipulation [5], [40] uses bilateral robots and gravity to expose corners.”
• Challenges and Outlook in Robotic Manipulation of Deformable Objects (2021, background): “The survey in [10] reviews and classifies the state-of-the-art according to the object’s physical properties.”