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

Tran Nguyen Le*, Fares Abu-Dakka, Ville Kyrki

*Corresponding author for this work

Research output: Contribution to conferencePaperScientificpeer-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
Number of pages4
Publication statusAccepted/In press - 7 May 2023
MoE publication typeNot Eligible
EventWorkshop on Representing and Manipulating Deformable Objects - ExCeL London, London, United Kingdom
Duration: 29 May 202329 May 2023


WorkshopWorkshop on Representing and Manipulating Deformable Objects
Country/TerritoryUnited Kingdom


  • Robotics
  • Deformable objects
  • Manipulation Planning
  • Deep Learning


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