LaSeSOM: A Latent and Semantic Representation Framework for Soft Object Manipulation

Peng Zhou, Jihong Zhu, Shengzeng Huo, David Navarro-Alarcon

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)


Soft object manipulation has recently gained popularity within the robotics community due to its potential applications in many economically important areas. Although great progress has been recently achieved in these types of tasks, most state-of-the-art methods are case-specific; They can only be used to perform a single deformation task (e.g., bending), as their shape representation algorithms typically rely on 'hard-coded' features. In this letter, we present LaSeSOM, a new feedback latent representation framework for semantic soft object manipulation. Our new method introduces internal latent representation layers between low-level geometric feature extraction and high-level semantic shape analysis; This allows the identification of each compressed semantic function and the formation of a valid shape classifier from different feature extraction levels. The proposed latent framework makes soft object representation more generic (independent from the object's geometry and its mechanical properties) and scalable (it can work with 1D/2D/3D tasks). Its high-level semantic layer enables to perform (quasi) shape planning tasks with soft objects, a valuable and underexplored capability in many soft manipulation tasks. To validate this new methodology, we report a detailed experimental study with robotic manipulators.

Original languageEnglish
Article number9410363
Pages (from-to)5381-5388
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number3
Publication statusPublished - Jul 2021


  • Bimanual manipulation
  • geodesic interpolation
  • latent space and manifolds
  • representation learning
  • shape deformation planning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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