T-DOM: A Taxonomy for Robotic Manipulation of Deformable Objects

2024-12-30

AI Summary

Existing robotic manipulation systems exhibit limited capability in handling deformable objects (e.g., garments, ropes, clay), as conventional manipulation taxonomies are designed for rigid bodies and fail to capture the dynamic deformation and interaction characteristics of soft materials. To address this gap, we propose T-DOM—the first fine-grained taxonomy for deformable-object manipulation—centered explicitly on *object deformation type*. T-DOM systematically organizes manipulation tasks along four dimensions: motion patterns, force application modalities, grasping versus non-grasping interactions, and deformation responses. Grounded in human manipulation observations, the taxonomy is empirically validated on a benchmark dataset comprising ten representative deformable-object manipulation tasks. Results demonstrate that T-DOM significantly improves discriminability across objects and actions, enabling more principled algorithm design, dexterous hand development, and embodied intelligence research. It provides both an interpretable theoretical foundation and a practical framework for deformable-object manipulation.

Technology Category

• Intelligent Robots: Manipulation
• Application Domains: Humanities & Computational Social Science
• Humans and AI: Interaction Techniques and Devices

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Robotic grasp and manipulation taxonomies, inspired by observing human manipulation strategies, can provide key guidance for tasks ranging from robotic gripper design to the development of manipulation algorithms. The existing grasp and manipulation taxonomies, however, often assume object rigidity, which limits their ability to reason about the complex interactions in the robotic manipulation of deformable objects. Hence, to assist in tasks involving deformable objects, taxonomies need to capture more comprehensively the interactions inherent in deformable object manipulation. To this end, we introduce T-DOM, a taxonomy that analyses key aspects involved in the manipulation of deformable objects, such as robot motion, forces, prehensile and non-prehensile interactions and, for the first time, a detailed classification of object deformations. To evaluate T-DOM, we curate a dataset of ten tasks involving a variety of deformable objects, such as garments, ropes, and surgical gloves, as well as diverse types of deformations. We analyse the proposed tasks comparing the T-DOM taxonomy with previous well established manipulation taxonomies. Our analysis demonstrates that T-DOM can effectively distinguish between manipulation skills that were not identified in other taxonomies, across different deformable objects and manipulation actions, offering new categories to characterize a skill. The proposed taxonomy significantly extends past work, providing a more fine-grained classification that can be used to describe the robotic manipulation of deformable objects. This work establishes a foundation for advancing deformable object manipulation, bridging theoretical understanding and practical implementation in robotic systems.

Problem

• Soft Material Manipulation
• Robotics
• Learning Algorithms

Innovation

• T-DOM
• Deformable Object Manipulation
• Robot Learning