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Object-centric task representation and transfer using diffused orientation fields.

Cem Bilaloglu1,2, Tobias Löw1,2, Sylvain Calinon1,2

  • 1Idiap Research Institute, 1920 Martigny, Switzerland.

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Robots can now transfer manipulation tasks across curved objects using diffused orientation fields. This new method creates smooth local reference frames, enabling robots to adapt tasks to different shapes effectively.

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision
  • Geometric Deep Learning

Background:

  • Transferring manipulation tasks to curved objects is challenging due to the lack of a global reference frame.
  • Task-relevant directions vary with object geometry, hindering generalization in robotics.

Purpose of the Study:

  • To develop a novel approach for expressing and transferring manipulation tasks across curved objects.
  • To enable robots to perform tasks on novel curved objects by overcoming geometric variations.

Main Methods:

  • Introduction of diffused orientation fields, a smooth representation of local reference frames.
  • Computation of the representation online from raw point cloud data using diffusion processes.
  • Conditioning the diffusion process on sparse keypoint correspondences for task transfer.

Main Results:

  • Successful transfer of continuous physical interaction tasks (coverage, slicing, peeling) across varied objects.
  • Demonstrated robustness under geometric, topological, and keypoint perturbations.
  • Reduced the task transfer problem to establishing sparse keypoint correspondences.

Conclusions:

  • Diffused orientation fields provide an effective representation for task transfer on curved objects.
  • The proposed method enables robust and generalized manipulation task execution in robotics.
  • This approach facilitates seamless task adaptation for robots interacting with complex geometries.