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Outline

Title
Abstract
FAQs
Introduction
Brief Overview of Analytic Approaches
Development of Data-Driven Methods
Analytic vs. Data-Driven Approaches
Classification of Data-Driven Approaches
From Low-Level Features to Grasp Hypotheses
From Global Shape to Grasp Hypothesis
Discussion and Conclusion
References
All Topics
Engineering
Mechanical Engineering

Data-Driven Grasp Synthesis—A Survey

Profile image of Tamim AsfourTamim Asfour

2014, IEEE Transactions on Robotics

https://doi.org/10.1109/TRO.2013.2289018
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21 pages

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Abstract

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally, for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.

FAQs

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What explains the limitations of analytic approaches in grasp synthesis?add

The limitations stem from simplified assumptions about contact models and compliance, leading to ambiguities in dynamic analysis (Bicchi & Kumar, 2000). Such assumptions may compromise robustness against positioning errors during robot operation.

How did data-driven methods evolve in the robotics community?add

Data-driven methods gained traction post-2004 with GraspIt! providing a platform for simulation and analysis of grasp synthesis. They incorporate learning from prior experiences, resulting in improved performance compared to classical approaches which struggle with unstructured environments.

What role do human demonstrations play in data-driven grasp synthesis?add

Human demonstrations provide vital training data that informs the learning of grasp configurations, though collecting such examples can be time-consuming (Saxena et al., 2008). Systems leveraging this data are shown to generalize grasp configurations effectively across similar objects.

When did machine learning approaches for grasp synthesis emerge?add

Machine learning approaches, particularly logistic regression for grasp point prediction, were notably advanced by Saxena et al. in 2008, enabling robots to interact with household objects like dishwashers. This marked a shift towards data-driven learning methodologies in robotic grasping.

What influences the sampling of grasp candidates in data-driven methods?add

Grasp candidate sampling is influenced by the representation of prior object knowledge and the underlying feature extraction methods, whether from 2D or 3D sensory data (Sahbani et al., 2015). The quality of candidates determines the selection process for feasible grasps.

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References (143)

  1. A. Sahbani, S. El-Khoury, and P. Bidaud, "An overview of 3d object grasp synthesis algorithms," Robot. Auton. Syst., vol. 60, no. 3, pp. 326-336, Mar. 2012.
  2. K. Shimoga, "Robot Grasp Synthesis Algorithms: A Survey," Int. Jour. of Robotic Research, vol. 15, no. 3, pp. 230-266, 1996.
  3. R. N. Murray, Z. Li, and S. Sastry, A Mathematical Introduction to Robotics Manipulation. CRC Press, 1994.
  4. A. Bicchi and V. Kumar, "Robotic grasping and contact," in IEEE Int. Conf. on Robotics and Automation (ICRA), San Francisco, Apr. 2000, invited paper.
  5. I. Kamon, T. Flash, and S. Edelman, "Learning to grasp using visual information," in IEEE Int. Conf. on Robotics and Automation (ICRA), 1994, pp. 2470-2476.
  6. S. Ekvall and D. Kragic, "Learning and Evaluation of the Approach Vector for Automatic Grasp Generation and Planning," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2007, pp. 4715-4720.
  7. A. Morales, T. Asfour, P. Azad, S. Knoop, and R. Dillmann, "Integrated Grasp Planning and Visual Object Localization For a Humanoid Robot with Five-Fingered Hands," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Beijing, China, Oct. 2006, pp. 5663-5668.
  8. D. Prattichizzo and J. Trinkle, Handbook of Robotics. Berlin, Heidelberg: Springer, 2008, ch. 28. Grasping, pp. 671-700.
  9. D. Prattichizzo, M. Malvezzi, M. Gabiccini, and A. Bicchi, "On the manipulability ellipsoids of underactuated robotic hands with compli- ance," Robotics and Autonomous Systems, vol. 60, no. 3, pp. 337 - 346, 2012.
  10. C. Rosales, R. Suarez, M. Gabiccini, and A. Bicchi, "On the Synthesis of Feasible and Prehensile Robotic Grasps," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2012.
  11. V.-D. Nguyen, "Constructing force-closure grasps," Int. Jour. Robotic Res., vol. 7, no. 3, pp. 3-16, 1988.
  12. M. A. Roa and R. Suárez, "Computation of independent contact regions for grasping 3-d objects," IEEE Trans. on Robotics, vol. 25, no. 4, pp. 839-850, 2009.
  13. R. Krug, D. N. Dimitrov, K. A. Charusta, and B. Iliev, "On the efficient computation of independent contact regions for force closure grasps." in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS). IEEE, 2010, pp. 586-591.
  14. A. Rodriguez, M. T. Mason, and S. Ferry, "From Caging to Grasping," in Robotics: Science and Systems (RSS), Apr. 2011.
  15. J. Seo, S. Kim, and V. Kumar, "Planar , Bimanual , Whole-Arm Grasping," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2012, pp. 3271-3277.
  16. L. E. Zhang and J. C. Trinkle, "The Application of Particle Filtering to Grasping Acquisition with Visual Occlusion and Tactile Sensing," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2012.
  17. C. Ferrari and J. Canny, "Planning optimal grasps," in IEEE Int. Conf. on Robotics and Automation (ICRA), vol. 3, may 1992, pp. 2290 -2295.
  18. A. T. Miller and P. K. Allen, "Graspit! a versatile simulator for robotic grasping," Robotics & Automation Magazine, IEEE, vol. 11, no. 4, pp. 110-122, 2004.
  19. A. T. Miller, S. Knoop, H. I. Christensen, and P. K. Allen, "Automatic Grasp Planning Using Shape Primitives," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2003, pp. 1824-1829.
  20. R. Pelossof, A. Miller, P. Allen, and T. Jebera, "An SVM learning approach to robotic grasping," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2004, pp. 3512-3518.
  21. C. Goldfeder, P. K. Allen, C. Lackner, and R. Pelossof, "Grasp Planning Via Decomposition Trees," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2007, pp. 4679-4684.
  22. C. Borst, M. Fischer, and G. Hirzinger, "Grasping the Dice by Dicing the Grasp," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2003, pp. 3692-3697.
  23. M. Ciocarlie and P. Allen, "Hand posture subspaces for dexterous robotic grasping," The Int. Jour. of Robotics Research (IJRR), vol. 28, pp. 851-867, July 2009.
  24. R. Diankov, "Automated construction of robotic manipulation pro- grams," Ph.D. dissertation, Carnegie Mellon University, Robotics In- stitute, Aug 2010.
  25. A. Morales, E. Chinellato, A. Fagg, and A. del Pobil, "Using Experi- ence for Assessing Grasp Reliability," Int. Jour. of Humanoid Robotics, vol. 1, no. 4, pp. 671-691, 2004.
  26. L. Montesano, M. Lopes, A. Bernardino, and J. Santos-Victor, "Learn- ing object affordances: From sensory-motor coordination to imitation," IEEE Trans. on Robotics, vol. 24, no. 1, pp. 15-26, Feb. 2008.
  27. R. Detry, E. Başeski, N. Krüger, M. Popović, Y. Touati, O. Kroemer, J. Peters, and J. Piater, "Learning object-specific grasp affordance densities," in IEEE Int. Conf. on Development and Learning, 2009, pp. 1-7.
  28. A. Saxena, J. Driemeyer, and A. Y. Ng, "Robotic grasping of novel objects using vision," The Int. Jour. of Robotics Research (IJRR), vol. 27, no. 2, pp. 157-173, Feb. 2008.
  29. A. Saxena, L. Wong, and A. Y. Ng, "Learning Grasp Strategies with Partial Shape Information," in AAAI Conf. on Artificial Intelligence, 2008, pp. 1491-1494.
  30. M. Stark, P. Lies, M. Zillich, J. Wyatt, and B. Schiele, "Functional Object Class Detection Based on Learned Affordance Cues," in Int. Conf. on Computer Vision Systems (ICVS), ser. LNAI, vol. 5008. Springer-Verlag, 2008, pp. 435-444.
  31. Q. V. Le, D. Kamm, A. F. Kara, and A. Y. Ng, "Learning to grasp objects with multiple contact points," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2010, pp. 5062-5069.
  32. J. Bohg and D. Kragic, "Learning grasping points with shape context," Robotics and Autonomous Systems, vol. 58, no. 4, pp. 362 -377, 2010.
  33. K. Hsiao, S. Chitta, M. Ciocarlie, and E. G. Jones, "Contact-reactive grasping of objects with partial shape information," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Taipei, Taiwan, Oct 2010, pp. 1228 -1235.
  34. E. Klingbeil, D. Rao, B. Carpenter, V. Ganapathi, A. Y. Ng, and O. Khatib, "Grasping with application to an autonomous checkout robot," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2011, pp. 2837-2844.
  35. R. B. Rusu, N. Blodow, and M. Beetz, "Fast point feature histograms (fpfh) for 3d registration," in IEEE Int. Conf. on Robotics and Automa- tion (ICRA), 2009, pp. 1848-1853.
  36. K. Lai, L. Bo, X. Ren, and D. Fox, "Sparse distance learning for object recognition combining rgb and depth information," in IEEE Int. Conf. on Robotics and Automation (ICRA), Shanghai, China, May 2011, pp. 4007-4013.
  37. J. Maitin-Shepard, M. Cusumano-Towner, J. Lei, and P. Abbeel, "Cloth grasp point detection based on Multiple-View geometric cues with application to robotic towel folding," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2010, pp. 2308 -2315.
  38. M. Beetz, U. Klank, I. Kresse, A. Maldonado, L. Mösenlechner, D. Pangercic, T. Rühr, and M. Tenorth, "Robotic Roommates Making Pancakes," in IEEE/RAS Int. Conf. on Humanoid Robots (Humanoids), Bled, Slovenia, Oct. 2011, pp. 529-536.
  39. R. Balasubramanian, L. Xu, P. D. Brook, J. R. Smith, and Y. Matsuoka, "Physical human interactive guidance: Identifying grasping principles from human-planned grasps," IEEE Trans. on Robotics, vol. 28, no. 4, pp. 899-910, Aug 2012.
  40. J. Weisz and P. K. Allen, "Pose Error Robust Grasping from Contact Wrench Space Metrics," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2012, pp. 557-562.
  41. K. Konolige, "Projected texture stereo," in IEEE Int. Conf. on Robotics and Automation (ICRA), May 2010, pp. 148-155.
  42. Willow Garage, "PR2," www.willowgarage.com/pages/pr2/overview.
  43. "ROS (Robot Operating System)," www.ros.org.
  44. Microsoft, "Kinect-Xbox.com," www.xbox.com/en-US/KINECT.
  45. PrimeSense, www.primesense.com.
  46. K. Lai, L. Bo, X. Ren, and D. Fox, "A Large-Scale Hierarchical Multi- View RGB-D Object Dataset," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2011, pp. 1817 -1824.
  47. J. Felip and A. Morales, "Robust sensor-based grasp primitive for a three-finger robot hand," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2009, pp. 1811-1816.
  48. K. Hsiao, P. Nangeroni, M. Huber, A. Saxena, and A. Y. Ng, "Reactive grasping using optical proximity sensors," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2009, pp. 2098-2105.
  49. P. Pastor, L. Righetti, M. Kalakrishnan, and S. Schaal, "Online Movement Adaptation based on Previous Sensor Experiences," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), San Francisco,USA, Sep 2011, pp. 365 -371.
  50. J. Romano, K. Hsiao, G. Niemeyer, S. Chitta, and K. Kuchenbecker, "Human-inspired robotic grasp control with tactile sensing," IEEE Trans. on Robotics, vol. 27, no. 6, pp. 1067 -1079, Dec 2011.
  51. M. A. Goodale, "Separate Visual Pathways for Perception and Action," Trends in Neurosciences, vol. 15, no. 1, pp. 20-25, 1992.
  52. U. Castiello, "The neuroscience of grasping." Nature Reviews Neuro- science, vol. 6, no. 9, pp. 726-736, 2005.
  53. J. C. Culham, C. Cavina-Pratesi, and A. Singhal, "The role of parietal cortex in visuomotor control: what have we learned from neuroimag- ing?" Neuropsychologia, vol. 44, no. 13, pp. 2668-2684, 2006.
  54. E. Chinellato and A. P. Del Pobil, "The neuroscience of vision-based grasping: a functional review for computational modeling and bio- inspired robotics." Jour. of Integrative Neuroscience, vol. 8, no. 2, pp. 223-254, 2009.
  55. J. Glover, D. Rus, and N. Roy, "Probabilistic models of object geometry for grasp planning," in Proceedings of Robotics: Science and Systems IV, Zurich, Switzerland, June 2008.
  56. M. Przybylski, T. Asfour, and R. Dillmann, "Planning grasps for robotic hands using a novel object representation based on the medial axis transform," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS). IEEE, sept. 2011, pp. 1781 -1788.
  57. M. A. Roa, M. J. Argus, D. Leidner, C. Borst, and G. Hirzinger, "Power Grasp Planning for Anthropomorphic Robot Hands," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2012.
  58. R. Detry, D. Kraft, A. G. Buch, N. Krüger, and J. Piater, "Refining grasp affordance models by experience," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2010, pp. 2287-2293.
  59. K. Huebner, K. Welke, M. Przybylski, N. Vahrenkamp, T. Asfour, D. Kragic, and R. Dillmann, "Grasping known objects with humanoid robots: A box-based approach," in Int. Conf. on Advanced Robotics (ICAR), 2009, pp. 1-6.
  60. P. Brook, M. Ciocarlie, and K. Hsiao, "Collaborative grasp planning with multiple object representations," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2011, pp. 2851 -2858.
  61. J. Romero, H. Kjellström, and D. Kragic, "Modeling and evaluation of human-to-robot mapping of grasps," in Int. Conf. on Advanced Robotics (ICAR). IEEE, 2009, pp. 228-233.
  62. C. Papazov, S. Haddadin, S. Parusel, K. Krieger, and D. Burschka, "Rigid 3d geometry matching for grasping of known objects in clut- tered scenes," Int. Jour. of Robotics Research (IJRR), vol. 31, no. 4, pp. 538-553, Apr. 2012.
  63. A. Collet Romea, D. Berenson, S. Srinivasa, and D. Ferguson, "Object recognition and full pose registration from a single image for robotic manipulation," in IEEE Int. Conf. on Robotics and Automation (ICRA), May 2009, pp. 48 -55.
  64. O. B. Kroemer, R. Detry, J. Piater, and J. Peters, "Combining active learning and reactive control for robot grasping," Robotics and Au- tonomous Systems, vol. 58, pp. 1105-1116, Sep 2010.
  65. J. Tegin, S. Ekvall, D. Kragic, B. Iliev, and J. Wikander, "Demon- stration based Learning and Control for Automatic Grasping," Jour. of Intelligent Service Robotics, vol. 2, no. 1, pp. 23-30, Aug 2008.
  66. F. Stulp, E. Theodorou, M. Kalakrishnan, P. Pastor, L. Righetti, and S. Schaal, "Learning motion primitive goals for robust manipulation," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), San Francisco,USA, Sep 2011, pp. 325 -331.
  67. K. Hübner and D. Kragic, "Selection of Robot Pre-Grasps using Box- Based Shape Approximation," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2008, pp. 1765-1770.
  68. M. Przybylski and T. Asfour, "Unions of balls for shape approximation in robot grasping," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS). Taipei, Taiwan: IEEE, Oct 2010, pp. 1592-1599.
  69. R. Diankov and J. Kuffner, "Openrave: A planning architecture for autonomous robotics," Robotics Institute, Pittsburgh, PA, Tech. Rep. CMU-RI-TR-08-34, July 2008.
  70. M. Do, J. Romero, H. Kjellström, P. Azad, T. Asfour, D. Kragic, and R. Dillmann, "Grasp Recognition and Mapping on Humanoid Robots," in IEEE/RAS Int. Conf. on Humanoid Robots (Humanoids), Paris, France, Dec 2009.
  71. A. Herzog, P. Pastor, M. Kalakrishnan, L. Righetti, T. Asfour, and S. Schaal, "Template-Based Learning of Grasp Selection," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2012.
  72. R. Detry, N. Pugeault, and J. Piater, "A probabilistic framework for 3D visual object representation," IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 31, no. 10, pp. 1790-1803, 2009.
  73. P. Azad, T. Asfour, and R. Dillmann, "Stereo-based 6d object local- ization for grasping with humanoid robot systems," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2007, pp. 919-924.
  74. T. Asfour, P. Azad, N. Vahrenkamp, K. Regenstein, A. Bierbaum, K. Welke, J. Schröder, and R. Dillmann, "Toward Humanoid Manip- ulation in Human-Centred Environments," Robotics and Autonomous Systems, vol. 56, pp. 54-65, Jan. 2008.
  75. M. Ciocarlie, K. Hsiao, E. G. Jones, S. Chitta, R. B. Rusu, and I. A. Sucan, "Towards reliable grasping and manipulation in household environments," in Int. Symposium on Experimental Robotics (ISER), New Delhi, India, Dec 2010.
  76. P. J. Besl and N. D. McKay, "A method for registration of 3-d shapes," IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 14, pp. 239-256, 1992.
  77. C. Papazov and D. Burschka, "An efficient ransac for 3d object recognition in noisy and occluded scenes." in ACCV (1), ser. Lecture Notes in Computer Science, R. Kimmel, R. Klette, and A. Sugimoto, Eds., vol. 6492. Springer, 2010, pp. 135-148.
  78. A. Collet Romea, M. Martinez Torres, and S. Srinivasa, "The moped framework: Object recognition and pose estimation for manipulation," Int. Jour. of Robotics Research (IJRR), vol. 30, no. 10, pp. 1284 - 1306, Sep 2011.
  79. J. Shotton, J. Winn, C. Rother, and A. Criminisi, "Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation," in European Conf. Computer Vision (ECCV), 2006, pp. 1-15.
  80. V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, "Groups of adjacent con- tour segments for object detection," IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 30, no. 1, pp. 36-51, 2008.
  81. S. Belongie, J. Malik, and J. Puzicha, "Shape Matching and Object Recognition Using Shape Contexts," IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 24, no. 4, pp. 509-522, 2002.
  82. C. Dance, J. Willamowski, L. Fan, C. Bray, and G. Csurka, "Visual categorization with bags of keypoints," in ECCV Int. Workshop on Statistical Learning in Computer Vision, 2004.
  83. F.-F. L. and P. Perona, "A bayesian hierarchical model for learning nat- ural scene categories," in IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR), vol. 2, 2005, pp. 524-531.
  84. B. Leibe, A. Leonardis, and B. Schiele, "An implicit shape model for combined object categorization and segmentation," in Toward Category-Level Object Recognition, 2006, pp. 508-524.
  85. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories," in IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, 2006, pp. 2169-2178.
  86. E. Rosch, C. B. Mervis, W. D. Gray, D. M. Johnson, and P. Boyes- Braem, "Basic objects in natural categories," Cognitive Psychology, vol. 8, no. 3, pp. 382-439, 1976.
  87. D. Song, C. H. Ek, K. Hübner, and D. Kragic, "Multivariate discretiza- tion for bayesian network structure learning in robot grasping," in IEEE Int. Conf. on Robotics and Automation (ICRA), Shanghai,China, May 2011, pp. 1944-1950.
  88. Y. Li and N. Pollard, "A Shape Matching Algorithm for Synthesizing Humanlike Enveloping Grasps," in IEEE/RAS Int. Conf. on Humanoid Robots (Humanoids), Dec. 2005, pp. 442-449.
  89. S. El-Khoury and A. Sahbani, "Handling Objects By Their Handles," in IROS-2008 Workshop on Grasp and Task Learning by Imitation, 2008.
  90. O. Kroemer, E. Ugur, E. Oztop, and J. Peters, "A Kernel-based Approach to Direct Action Perception," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2012.
  91. R. Detry, C. H. Ek, M. Madry, J. Piater, and D. Kragic, "Generalizing grasps across partly similar objects," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2012, pp. 3791-3797.
  92. --, "Learning a Dictionary of Prototypical Grasp-predicting Parts from Grasping Experience," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2013, to appear.
  93. A. Ramisa, G. Alenyà, F. Moreno-Noguer, and C. Torras, "Using depth and appearance features for informed robot grasping of highly wrinkled clothes," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2012, pp. 1703-1708.
  94. A. Boularias, O. Kroemer, and J. Peters, "Learning robot grasping from 3-d images with markov random fields," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2011, pp. 1548-1553.
  95. L. Montesano and M. Lopes, "Active learning of visual descriptors for grasping using non-parametric smoothed beta distributions," Robotics and Autonomous Systems, vol. 60, pp. 452-462, 2012.
  96. D. Fischinger and M. Vincze, "Empty the basket -a shape based learning approach for grasping piles of unknown objects," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Oct 2012, pp. 2051-2057.
  97. N. Bergström, J. Bohg, and D. Kragic, "Integration of visual cues for robotic grasping," in Computer Vision Systems, ser. Lecture Notes in Computer Science, vol. 5815. Springer Berlin / Heidelberg, 2009, pp. 245-254.
  98. U. Hillenbrand and M. A. Roa, "Transferring functional grasps through contact warping and local replanning," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Oct 2012, pp. 2963-2970.
  99. J. Bohg, K. Welke, B. León, M. Do, D. Song, W. Wohlkinger, M. Madry, A. Aldóma, M. Przybylski, T. Asfour, H. Martí, D. Kragic, A. Morales, and M. Vincze, "Task-based grasp adaptation on a hu- manoid robot," in Int. IFAC Symposium on Robot Control (SYROCO), Dubrovnik,Croatia, Sep 2012, pp. 852 -859.
  100. N. Curtis and J. Xiao, "Efficient and effective grasping of novel objects through learning and adapting a knowledge base," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), sept. 2008, pp. 2252 -2257.
  101. C. Goldfeder and P. Allen, "Data-driven grasping," Autonomous Robots, vol. 31, pp. 1-20, 2011.
  102. Z. C. Marton, D. Pangercic, N. Blodow, and M. Beetz, "Combined 2D-3D Categorization and Classification for Multimodal Perception Systems," Int. Jour. of Robotics Research (IJRR), vol. 30, no. 11, pp. 1378-1402, 2011.
  103. D. Rao, Q. V. Le, T. Phoka, M. Quigley, A. Sudsang, and A. Y. Ng, "Grasping novel objects with depth segmentation," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Taipei, Taiwan, Oct 2010, pp. 2578-2585.
  104. J. Speth, A. Morales, and P. J. Sanz, "Vision-Based Grasp Planning of 3D Objects by Extending 2D Contour Based Algorithms," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2008, pp. 2240- 2245.
  105. M. Madry, D. Song, and D. Kragic, "From object categories to grasp transfer using probabilistic reasoning," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2012, pp. 1716-1723.
  106. H. Dang and P. K. Allen, "Semantic grasping: Planning robotic grasps functionally suitable for an object manipulation task," in IEEE Int. Conf. on Intelligent Robots and Systems (IROS), 2012, pp. 1311-1317.
  107. P. A. Viola and M. J. Jones, "Rapid object detection using a boosted cascade of simple features," in IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR), 2001, pp. 511-518.
  108. Z. C. Marton, R. B. Rusu, D. Jain, U. Klank, and M. Beetz, "Probabilis- tic categorization of kitchen objects in table settings with a composite sensor," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), St. Louis, MO, USA, Oct 2009, pp. 4777 -4784.
  109. D. G. Lowe, "Object Recognition from Local Scale-Invariant Features," in Int. Conf. on Computer Vision, ser. ICCV '99, vol. 2. Washington, DC, USA: IEEE Computer Society, 1999.
  110. Z. C. Marton, D. Pangercic, N. Blodow, J. Kleinehellefort, and M. Beetz, "General 3D Modelling of Novel Objects from a Single View," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Taipei, Taiwan, Oct 2010, pp. 3700 -3705.
  111. W. Wohlkinger and M. Vincze, "Shape-based depth image to 3d model matching and classification with inter-view similarity," in IEEE Int. Conf. on Robotics and Automation (ICRA), San Francisco, USA, Sept 2011, pp. 4865-4870.
  112. A. Aldoma and M. Vincze, "Pose alignment for 3d models and single view stereo point clouds based on stable planes," Int. Conf. on 3D Imaging, Modeling, Processing, Visualization and Transmission, vol. 0, pp. 374-380, 2011.
  113. R. B. Rusu, G. Bradski, R. Thibaux, and J. Hsu, "Fast 3d recognition and pose using the viewpoint feature histogram," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Taipei, Taiwan, Oct 2010, pp. 2155 -2162.
  114. R. B. Rusu, A. Holzbach, G. Bradski, and M. Beetz, "Detecting and segmenting objects for mobile manipulation," in Proceedings of IEEE Workshop on Search in 3D and Video (S3DV), held in conjunction with the 12th IEEE Int. Conf. on Computer Vision (ICCV), Kyoto, Japan, Sep 2009.
  115. D. I. Gonzalez-Aguirre, J. Hoch, S. Rohl, T. Asfour, E. Bayro- Corrochano, and R. Dillmann, "Towards shape-based visual object categorization for humanoid robots." in IEEE Int. Conf. on Robotics and Automation (ICRA). IEEE, 2011, pp. 5226-5232.
  116. D. Kraft, N. Pugeault, E. Baseski, M. Popovic, D. Kragic, S. Kalkan, F. Wörgötter, and N. Krueger, "Birth of the object: Detection of object- ness and extraction of object shape through object action complexes," Int. Jour. of Humanoid Robotics, pp. 247-265, 2009.
  117. M. Popović, G. Kootstra, J. A. Jørgensen, D. Kragic, and N. Krüger, "Grasping unknown objects using an early cognitive vision system for general scene understanding," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), San Francisco, USA, Sep 2011, pp. 987 -994.
  118. G. M. Bone, A. Lambert, and M. Edwards, "Automated Modelling and Robotic Grasping of Unknown Three-Dimensional Objects," in IEEE Int. Conf. on Robotics and Automation (ICRA), Pasadena, CA, USA, May 2008, pp. 292-298.
  119. M. Richtsfeld and M. Vincze, "Grasping of Unknown Objects from a Table Top," in ECCV Workshop on 'Vision in Action: Efficient strategies for cognitive agents in complex environments', Marseille, France, Sep 2008.
  120. J. Bohg, M. Johnson-Roberson, B. León, J. Felip, X. Gratal, N. Bergström, D. Kragic, and A. Morales, "Mind the Gap -Robotic Grasping under Incomplete Observation," in IEEE Int. Conf. on Robotics and Automation (ICRA), May 2011.
  121. J. Stückler, R. Steffens, D. Holz, and S. Behnke, "Real-Time 3D Perception and Efficient Grasp Planning for Everyday Manipulation Tasks," in European Conf. on Mobile Robots (ECMR), Örebro, Sweden, Sep 2011.
  122. A. Maldonado, U. Klank, and M. Beetz, "Robotic grasping of un- modeled objects using time-of-flight range data and finger torque information," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Oct. 2010, pp. 2586-2591.
  123. V. Lippiello, F. Ruggiero, B. Siciliano, and L. Villani, "Visual grasp planning for unknown objects using a multifingered robotic hand," IEEE/ASME Trans. on Mechatronics, vol. 18, no. 3, pp. 1050-1059, June.
  124. C. Dunes, E. Marchand, C. Collowet, and C. Leroux, "Active Rough Shape Estimation of Unknown Objects," in IEEE Int. Conf. on Intelli- gent Robots and Systems (IROS), 2008, pp. 3622-3627.
  125. B. Kehoe, D. Berenson, and K. Goldberg, "Toward Cloud-Based Grasping with Uncertainty in Shape: Estimating Lower Bounds on Achieving Force Closure with Zero-Slip Push Grasps," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2012, pp. 576-583.
  126. A. Morales, P. J. Sanz, A. P. del Pobil, and A. H. Fagg, "Vision-based three-finger grasp synthesis constrained by hand geometry," Robotics and Autonomous Systems, vol. 54, no. 6, pp. 496 -512, 2006.
  127. K. Hsiao, M. Ciocarlie, and P. Brook, "Bayesian grasp planning," in ICRA 2011 Workshop on Mobile Manipulation: Integrating Perception and Manipulation, 2011.
  128. A. Aydemir and P. Jensfelt, "Exploiting and modeling local 3d structure for predicting object locations," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2012.
  129. G. Metta and P. Fitzpatrick, "Better Vision through Manipulation," Adaptive Behavior, vol. 11, no. 2, pp. 109-128, 2003.
  130. J. Kenney, T. Buckley, and O. Brock, "Interactive segmentation for manipulation in unstructured environments," in IEEE Int. Conf. on Robotics and Automation (ICRA), ser. ICRA'09, 2009, pp. 1343-1348.
  131. N. Bergström, C. H. Ek, M. Björkman, and D. Kragic, "Generating object hypotheses in natural scenes through human-robot interaction," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), San Francisco,USA, Sep 2011, pp. 827-833.
  132. D. Schiebener, A. Ude, J. Morimoto, T. Asfour, and R. Dillmann, "Segmentation and learning of unknown objects through physical in- teraction," in IEEE/RAS Int. Conf. on Humanoid Robots (Humanoids), oct. pp. 500 -506.
  133. M. Tenorth, A. C. Perzylo, R. Lafrenz, and M. Beetz, "The RoboEarth language: Representing and Exchanging Knowledge about Actions, Objects, and Environments," in IEEE Int. Conf. on Robotics and Automation (ICRA), St. Paul, MN, USA, May 2012, pp. 1284-1289.
  134. Y. Bekiroglu, R. Detry, and D. Kragic, "Joint observation of object pose and tactile imprints for online grasp stability assessment," in Manipulation Under Uncertainty (ICRA 2011 Workshop), 2011.
  135. N. Hudson, T. Howard, J. Ma, A. Jain, M. Bajracharya, S. Myint, C. Kuo, L. Matthies, P. Backes, P. Hebert, T. Fuchs, and J. Burdick, "End-to-End Dexterous Manipulation with Deliberate Interactive Esti- mation," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2012, pp. 2371-2378.
  136. M. Kazemi, J.-S. Valois, J. A. D. Bagnell, and N. Pollard, "Robust object grasping using force compliant motion primitives," in Robotics: Science and Systems (RSS), Sydney, Australia, July 2012.
  137. X. Gratal, J. Romero, J. Bohg, and D. Kragic, "Visual servoing on unknown objects," Mechatronics, vol. 22, no. 4, pp. 423 -435, 2012.
  138. C. Goldfeder, M. Ciocarlie, H. Dang, and P. K. Allen, "The columbia grasp database," in IEEE Int. Conf. on Robotics and Automation (ICRA), 2009, pp. 3343-3349.
  139. Maersk Mc-Kinney Møller Instituttet, University of Southern Denmark, "VisGraB-a benchmark for vision-based grasping of unknown objects," www.robwork.dk/visgrab.
  140. Healthcare Robotics Labs, Georgia Institute of Technology, "Pr2 playpen," ros.org/wiki/pr2_playpen.
  141. DARPA, "ARM | Autonomous Robotic Manipulation," www. thearmrobot.com.
  142. "RoboCup@Home," www.ai.rug.nl/robocupathome.
  143. S. Ulbrich, D. Kappler, T. Asfour, N. Vahrenkamp, A. Bierbaum, M. Przybylski, and R. Dillmann, "The OpenGRASP benchmarking suite: An environment for the comparative analysis of grasping and dexterous manipulation," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), sept. 2011, pp. 1761 -1767.

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