Knowledge-based reasoning from human grasp demonstrations for robot grasp synthesis

Robotics and Autonomous Systems

Volumn 62, Issue 6, 2014, Pages 794-817

  Faria, Diego R ^a   Trindade, Pedro ^a,b   Lobo, Jorge ^a   Dias, Jorge ^a,c  

^a UNIVERSITY OF COIMBRA   (Portugal)

^b CITARD SERVICES LTD   (Cyprus)

^c KHALIFA UNIVERSITY   (United Arab Emirates)

Author keywords

Human grasp demonstrations; Object shape representation; Probabilistic inference; Robot grasp synthesis

Indexed keywords

AGRICULTURAL ROBOTS; DEMONSTRATIONS; OBJECT RECOGNITION; PROBABILITY DISTRIBUTIONS; ROBOT LEARNING; ROBOTIC ARMS; ROBOTICS;

COMPLEX ENVIRONMENTS; GEOMETRICAL APPROACHES; GRASP SYNTHESIS; KNOWLEDGE-BASED REASONING; MULTI-SENSOR INFORMATION; OBJECT SHAPE; PROBABILISTIC DISTRIBUTION; PROBABILISTIC INFERENCE;

KNOWLEDGE BASED SYSTEMS;

EID: 84899576688     PISSN: 09218890     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.robot.2014.02.003     Document Type: Article

References (54)

1. Grasp project: emergence of cognitive grasping through introspection, emulation and surprise, http://grasp.xief.net/.
2. Shadow robot dextrous hand, www.shadowrobot.com.
3. J. Bohg, A. Morales, T. Asfour, and D. Kragic Data-driven grasp synthesis - a survey IEEE Trans. Robot. 2013
4. S. Calinon, F. Guenter, and A. Billard On learning, representing, and generalizing a task in a humanoid robot IEEE Trans. Syst. Man Cybern. B 37 2 2007 286 298
5. J.J. Steil, F. Röthling, R. Haschke, and H. Ritter Situated robot learning for multi-modal instruction and imitation of grasping Robot. Auton. Syst. 47 2004 129 141
6. E. Oztop, and M.A. Arbib Schema design and implementation of the grasp-related mirror neuron system Biol. Cybernet. 87 2 2002 116 140
7. M. Fischer, P.V. der Smagt, G. Hirzinger, Learning techniques in a dataglove based telemanipulation system for the dlr hand, in: IEEE International Conference on Robotics and Automation, 1998.
8. S. Ekvall, D. Kragic, Interactive grasp learning based on human demonstration, in: IEEE/RSJ International Conference on Robotics and Automation, 2004.
9. J. Aleotti, S. Caselli, Programming task-oriented grasps by demonstration in virtual reality, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, WS on Grasp and Task Learning by Imitation, 2008.
10. M. Hueser, T. Baier, J. Zhang, Learning of demonstrated grasping skills by stereoscopic tracking of human hand configuration, in: IEEE International Conference on Robotics and Automation, 2006.
11. J. Romero, H. Kjellstrm, D. Kragic, Human-to-robot mapping of grasps, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, WS on Grasp and Task Learning by Imitation, 2008.
12. A. Bierbaum, K. Welke, D. Burger, T. Asfour, R. Dillmann, A framework for visually guided haptic exploration with five finger hands, in: Proceedings of the Robotics: Science & Systems, Manipulation Workshop - Sensing and Adapting to the Real World, 2007.
13. K. Muelling, J. Kober, O. Kroemer, and J. Peters Learning to select and generalize striking movements in robot table tennis Int. J. Robot. Res. 32 2013 263 279
14. V. Kruger, D. Herzog, S. Baby, A. Ude, and D. Kragic Learning actions from observations IEEE Robot. Autom. Mag. 17 2 2010 30 43
15. L. Montesano, M. Lopes, A. Bernardino, and J. Santos-Victor Learning object affordances: from sensory motor maps to imitation IEEE Trans. Robot. 24 1 2008 15 26
16. R. Pelossof, A. Miller, P. Allen, T. Jebara, An svm learning appraoch to robotic grasping, in: IEEE International Conference on Robotics and Automation, 2004.
17. Y. Li, N. Pollard, A shape matching algorithm for synthesizing humanlike enveloping grasps, in: 5th IEEE-RAS Int. Conf. on Humanoid Robots, 2005, pp. 442-449.
18. J. Bohg, and D. Kragic Learning grasping points with shape context Robot. Auton. Syst. 58 4 2010 362 377
19. A. Saxena, J. Driemeyer, J. Kearns, and A.Y. Ng Robotic grasping of novel objects Neural Inf. Process. Syst. 19 2007 1209 1216
20. M. Stark, M.Z.P. Lies, and B. Schiele Functional object class detection based on learned affordance cues Comput. Vis. Syst. 2008 435 444
21. A.T. Miller, S. Knoop, H.I. Christensen, P.K. Allen, Automatic grasp planning using shape primitives, in: Proceeding of International Conference on Robotics and Automation, ICRA 2003, 14-19 Sep., Tapei, 2003, pp. 1824-1829.
22. C. Goldfeder, P.K. Allen, C. Lackner, R. Pelossof, Grasp planning via decomposition trees, in: IEEE Int. Conf. on Robotics and Automation, 2007.
23. E. Lopez-Damian, D. Sidobre, R. Alami, Grasp planning for non-convex objects, in: 36th International Symposium on Robotics, ISR, 2005.
24. G. Biegelbauer, M. Vincze, Efficient 3d object detection by fitting superquadrics to range image data for robot's object manipulation, in: international Conference on Robotics and Automation, ICRA'07, 2007.
25. S. El-Khoury, and A. Sahbani A new strategy combining empirical and analytical approaches for grasping unknown 3d objects Robot. Auton. Syst. 58 5 2010 497 507
26. A. Sahbani, S. El-Khoury, A hybrid approach for grasping 3d objects, in: IEEE/RSJ Int. Conf. intelligent robots and Systems, St. Louis, USA, 2009.
27. T.A.P. Azad, R. Dillmann, Stereo-based 6d object localization for grasping with humanoid robot, in: In IEEE International Conference on Intelligent Robots and Systems, IROS 2007, San Diego, USA, 2007.
28. N.B.S. Rodrguez-Jiménez, M. Abderrahim, 3d object reconstruction with a single rgb-depth image, in: International Conference on Computer Vision Theory and Applications, VISAPP, Barcelona, Spain, 2013.
29. U. Prieur, V. Perdereau, A. Bernardino, Modeling and planning high-level in-hand manipulation actions from human knowledge and active learning from demonstration, in: IEEE International Conference on Intelligent Robots and Systems, 2012.
30. J.A.C. Ramon, F.T. Medina, and V. Perdereau Finger readjustment algorithm for object manipulation based on tactile information Int. J. Adv. Robot. Syst. 2013
31. C. Rosales, R. Suarez, M. Gabiccini, A. Bicchi, On the synthesis of feasible and prehensile robotic grasps, in: IEEE International Conference on Robotics and Automation, 2012.
32. M. Gabiccini, A. Bicchi, D. Prattichizzo, and M. Malvezzi On the role of hand synergies in the optimal choice of grasping forces Auton. Robots 31 2011 235 252
33. L. Chen, and N. Georganas An efficient and robust algorithm for 3d mesh segmentation Multimedia Tools Appl. 29 2 2006 109 125
34. I. Biederman Recognition-by-components: a theory of human image understanding Psychol. Rev. 94 1987 115 147
35. D.R. Faria, R. Martins, J. Lobo, J. Dias, Probabilistic representation of 3d object shape by in-hand exploration, in: IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Taipei, Taiwan, 2010.
36. D.R. Faria, R. Martins, J. Lobo, and J. Dias Extracting data from human manipulation of objects towards improving autonomous robotic grasping Robot. Auton. Syst. 60 3 2012 396 410 Special Issue on autonomous grasping
37. A.T. Miller, Graspit!: a versatile simulator for robotic grasping, Ph.D. thesis, Ph.D. Thesis, Department of Computer Science, Columbia University, June 2001.
38. M. Santello, M. Flanders, and J.F. Soechting Postural hand synergies for tool use J. Neurosci. 18 1998 105 115
39. D.R. Faria, J. Lobo, J. Dias, Identifying objects from hand configurations during in-hand exploration, in: 2012 IEEE International Conference on Multisensor Fusion and Information Integration, IEEE MFI 2012, Hamburg, September, 2012.
40. G. Rissanen Modeling the shortest data description Automatica 14 1978 465 471
41. M.R. Gupta, and Y. Chen Theory and use of the em algorithm Found. Trends Signal Process. 4 3 2011 223 296
42. A.H. Barr Superquadrics and angle preserving transformations IEEE Computer Graphics and Applications 1981
43. F. Solina, A. Leonardis, A. Macerl, A direct part-level segmentation of range images using volumetric models, in: IEEE International Conference on Robotics and Automation, 1994.
44. L. Chevalier, F. Jaillet, and A. Baskurt Segmentation and superquadric modeling of 3d objects WSCG 11 2003 232 239
45. P. Drews Jr., P. Nunez, R. Rocha, M. Campos, J. Dias, Novelty detection and 3d shape retrieval using superquadrics and multi-scale sampling for autonomous mobile robots, in: Proc. of IEEE ICRA'10, 2010.
46. A. Jaklic, A. Leonardis, and F. Solina Segmentation and Recovery of Superquadrics Computational Imaging and Vision vol. 20 2000 Kluwer Dordrecth 0-7923-6601-8
47. D.R. Faria, R. Martins, J. Lobo, J. Dias, A probabilistic framework to detect suitable grasping regions on objects, in: 10th IFAC Symposium on Robot Control, SYROCO'12, Dubrovinik, Croatia, Sept., 2012.
48. Handle project - data collection database, 2012. URL: http://paloma.isr.uc.pt/datacollectiondb/handle.
49. Handle project, 2013 - developmental pathway towards autonomy and dexterity in robot in-hand manipulation, 2013. http://www.handle-project.eu/.
50. Pcl - point cloud library, 2013. http://pointclouds.org.
51. M.A. Fischler, and R.C. Bolles Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography Commun. ACM 24 6 1981 381 395
52. F. Veiga, A. Bernardino, Towards bayesian grasp optimization with wrench space analysis, in: IEEE IROS 2012 Workshop "Beyond Robot Grasping", Vilamoura, Portugal, October, 2012.
53. M. Ciocarlie, C. Goldfeder, P. Allen, Dimensionality reduction for hand-independent dexterous robotic grasping, in: 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007, pp. 3270-3275.
54. N. Hendrich, Deliverable 24: parameterizing and creating new actions, in: HANDLE project, D24, 2012. http://www.handle-project.eu/.