CITR: A Coordinate-Invariant Task Representation for Robotic Manipulation

Peter So, Rafael I. Cabral Muchacho, Robin Jeanne Kirschner, Abdalla Swikir, Luis Figueredo, Fares J. Abu-Dakka, Sami Haddadin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The basis for robotics skill learning is an adequate representation of manipulation tasks based on their physical properties. As manipulation tasks are inherently invariant to the choice of reference frame, an ideal task representation would also exhibit this property. Nevertheless, most robotic learning approaches use unprocessed, coordinate-dependent robot state data for learning new skills, thus inducing challenges regarding the interpretability and transferability of the learned models.In this paper, we propose a transformation from spatial measurements to a coordinate-invariant feature space, based on the pairwise inner product of the input measurements. We describe and mathematically deduce the concept, establish the task fingerprints as an intuitive image-based representation, experimentally collect task fingerprints, and demonstrate the usage of the representation for task classification. This representation motivates further research on data-efficient and transferable learning methods for online manipulation task classification and task-level perception.

Original languageEnglish (US)
Title of host publication2024 IEEE International Conference on Robotics and Automation, ICRA 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages17501-17507
Number of pages7
ISBN (Electronic)9798350384574
DOIs
StatePublished - 2024
Event2024 IEEE International Conference on Robotics and Automation, ICRA 2024 - Yokohama, Japan
Duration: May 13 2024May 17 2024

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2024 IEEE International Conference on Robotics and Automation, ICRA 2024
Country/TerritoryJapan
CityYokohama
Period5/13/245/17/24

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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