SPONGE: Sequence Planning with Deformable-ON-Rigid Contact Prediction from Geometric Features

Tran Nguyen Le, Fares Abu-Dakka, Ville Kyrki

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


Planning robotic manipulation tasks, especially those that involve interaction between deformable and rigid objects, is challenging due to the complexity in predicting such interactions. We introduce SPONGE, a sequence planning pipeline powered by a deep learning-based contact prediction model for contacts between deformable and rigid bodies under interactions. The contact prediction model is trained on synthetic data generated by a developed simulation environment to learn the mapping from point-cloud observation of a rigid target object and the pose of a deformable tool, to 3D representation of the contact points between the two bodies. We experimentally evaluated the proposed approach for a dish cleaning task both in simulation and on a real \panda with real-world objects. The experimental results demonstrate that in both scenarios the proposed planning pipeline is capable of generating high-quality trajectories that can accomplish the task by achieving more than 90\% area coverage on different objects of varying sizes and curvatures while minimizing travel distance. Code and video are available at: \url{this https URL}.
Original languageEnglish
Title of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
Number of pages8
ISBN (Electronic)978-1-6654-9190-7
Publication statusPublished - 13 Dec 2023
MoE publication typeA4 Conference publication
EventIEEE/RSJ International Conference on Intelligent Robots and Systems
- Detroit, United States
Duration: 1 Oct 20235 Oct 2023

Publication series

Name Proceedings of the IEEE/RSJ international conference on intelligent robots and systems
ISSN (Electronic)2153-0866


ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS
Country/TerritoryUnited States


  • Robotics
  • Deformable objects
  • Manipulation Planning
  • Deep Learning
  • Robotic manipulation


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