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estevez2017icasrc-unfolding-presentation

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Presentation corresponding to "Improving and Evaluating Robotic Garment Unfolding: A Garment-Agnostic Approach".

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estevez2017icasrc-unfolding-presentation

  1. 1. Improving and Evaluating Robotic Garment Unfolding: A Garment-Agnostic Approach David Estevez*, Raul Fernandez-Fernandez, Juan G. Victores, Carlos Balaguer Robotics Lab research Group Universidad Carlos III de Madrid {destevez, rauferna, jcgvicto, balaguer}@ing.uc3m.es
  2. 2. /25 About me David Estevez Ph.D candidate at Carlos III University of Madrid (Spain) Thesis topic: robotic systems for the perception and manipulation of deformable objects (such a garments) 2
  3. 3. /25 Structure Introduction to the problem Proposed solution Results and conclusions 3
  4. 4. /25 Introduction Problem: Unfolding Garments 4
  5. 5. /25 Introduction 5 Typical laundry pipeline 1.Pick a Garment 2.Unfold Garment 3.Iron Garment 4.Fold Garment
  6. 6. /25 Introduction Human-based Large machines Actual need in industrial environments 6
  7. 7. /25 Introduction Actual need in domestic environments 7 Elder people Lazy peopleDisabled people
  8. 8. /25 Introduction CloPeMa (Clothes Perception and Manipulation) European Project (2012-2015) (Doumanoglou et al., 2014) 8
  9. 9. /25 State of the Art 9 Two approaches: Modeling-Based Approaches Manipulation-Based Approaches
  10. 10. /25 State of the Art Modeling-Based Approaches (Miller et al., 2011) 10
  11. 11. /25 State of the Art Manipulation-Based Approaches 11 (Cusumano-Towner et al., 2011)
  12. 12. /25 Architecture 12
  13. 13. /25 Algorithm 13 Contributions (with respect to our previous work): ● Input data is now a 3D mesh reconstructed by Kinect Fusion. ● Segmentation is performed with RANSAC (color independent). ● Manipulation of the garment is performed with place point symmetrical to fold edge. ● Experiments executed with industrial manipulator to obtain quantitative results.
  14. 14. /25 Algorithm 14 Assumptions: ● A single garment has been already selected from a pile of (unordered) garments. ● The garment has been laid as flat as possible with a simple manipulation operation (2 random grasping points).
  15. 15. /25 Architecture 15 Garment scan RGB-D Sensor
  16. 16. /25 Architecture 16 Garment scan (II)
  17. 17. /25 Architecture 17 Garment segmentation Segmentation mask generated by RANSAC Garment simplified contour
  18. 18. /25 Architecture 18 Garment depth map clustering Watershed algorithm Height clusters
  19. 19. /25 Architecture 19 Garment pick and place points Selection based on Bumpiness:
  20. 20. /25 Experiments and Results 20 ●Experiments performed with an ABB IRB 240 industrial manipulator. ●6 garment categories, 5 trials per category (3 with 1 fold and 2 with 2 folds). ●Comparison between old approach and new approach.
  21. 21. /25 Experiments and Results 21
  22. 22. /25 Experiments and Results 22
  23. 23. /25 Conclusions Performance improvement with respect to old approach (20%) Very thin garments still yield bad results due to sensor resolution 23
  24. 24. /25 Future work Improve segmentation of thin garments with color information. Experiment with different clustering algorithms. Improve manipulation with tactile feedback, orientation control. 24
  25. 25. Improving and Evaluating Robotic Garment Unfolding: A Garment-Agnostic Approach Thank you! David Estevez*, Raul Fernandez-Fernandez, Juan G. Victores, Carlos Balaguer

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