LaSeSOM: A Latent and Semantic Representation Framework for Soft Object Manipulation
This work provides a more generic and scalable representation for soft object manipulation, which is valuable for roboticists working on diverse deformation tasks.
The paper addresses the limitation of case-specific methods in soft object manipulation by introducing LaSeSOM, a latent and semantic representation framework. This framework allows for generic and scalable soft object representation, enabling shape planning tasks independent of object geometry and mechanical properties.
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 paper, 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.