Data-driven grasping

Autonomous Robots

Volumn 31, Issue 1, 2011, Pages 1-20

  Goldfeder, Corey ^a   Allen, Peter K ^a  

^a Columbia University ^*  (United States)

Author keywords

Data driven; Grasping; Robotics

Indexed keywords

3D MODELS; DATA-DRIVEN; GRASPING; MATCHED MODELS; MULTIPLE METHODS; SENSOR DATA;

SENSORS;

THREE DIMENSIONAL;

EID: 79953858685     PISSN: 09295593     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10514-011-9228-1     Document Type: Article

References (46)

1. Aleotti, J., & Caselli, S. (2007). Robot grasp synthesis from virtual demonstration and topology-preserving environment reconstruction. In IEEE international conference on intelligent robots and systems.
2. Aydin, Y., & Nakajima, M. (1999). Database guided computer animation of human grasping using forward and inverse kinematics. Computers & Graphics, 23 (1).
3. Balasubramanian, R., Xu, L., Brook, P. D., Smith, J. R., & Matsuoka, Y. (2010). Human-guided grasp measures improve grasp robustness on physical robot. In IEEE international conference on robotics and automation.
4. Belongie, S., & Malik, J. (2000). Matching with shape contexts. In Workshop on content-based access of image and video libraries.
5. Biederman, I. (1995). Visual object recognition. In S. F. Kosslyn & D. N. Osherson (Eds.), An invitation to cognitive science: Vol. 2 (2nd ed.). Chap. 4.
6. Bohg, J., & Kragic, D. (2010). Learning grasping points with shape context. Robotics and Autonomous Systems, 58 (4).
7. Bowers, D. L., & Lumia, R. (2003). Manipulation of unmodeled objects using intelligent grasping schemes. IEEE Transactions on Fuzzy Systems, 11 (3).
8. Chen, D.-Y., Ouhyoung, M., Tian, X. P., & Shen, Y. T. (2003). On visual similarity based 3d model retrieval. In Eurographics.
9. Ciocarlie, M., & Allen, P. K. (2009). Hand posture subspaces for dexterous robotic grasping. The International Journal of Robotics Research, 28, 851-867.
10. Ciocarlie, M., Goldfeder, C., & Allen, P. K. (2007a). Dimensionality reduction for hand-independent dexterous robotic grasping. In IEEE international conference on intelligent robots and systems.
11. Ciocarlie, M., Lackner, C., & Allen, P. K. (2007b). Soft finger model with adaptive contact geometry for grasping and manipulation tasks. In IEEE world haptics.
12. Dueck, G., & Scheuer, T. (1990). Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing. Journal of Computational Physics, 90 (1).
13. Ferrari, C., & Canny, J. (2002). Planning optimal grasps. In IEEE international conference on robotics and automation.
14. Gelfand, N., Mitra, N. J., Guibas, L. J., & Pottmann, H. (2005). Robust global registration. In Symposium on geometry processing.
15. Glover, J., Rus, D., & Roy, N. (2008). Probabilistic models of object geometry for grasp planning. In Robotics: science and systems.
16. Goldfeder, C., & Allen, P. K. (2008). Autotagging to improve text search for 3d models. In Joint conference on digital libraries.
17. Goldfeder, C., Ciocarlie, M., Dang, H., & Allen, P. K. (2009a). The Columbia grasp database. In IEEE international conference on robotics and automation.
18. Goldfeder, C., Ciocarlie, M., Peretzman, J., Dang, H., & Allen, P. K. (2009b). Data-driven grasping with partial sensor data. In IEEE international conference on intelligent robots and systems.
19. Huber, D. F., & Hebert, M. (2003). Fully automatic registration of multiple 3d data sets. Image and Vision Computing, 21 (7).
20. Jaccard, P. (1901). Étude comparative de la distribution florale dans une portion des alpes et des jura. Bulletin del la Société Vaudoise des Sciences Naturelles, 37.
21. Johnson, A. E., & Hebert, M. (1997). Surface registration by matching oriented points. In 3-D digital imaging and modeling.
22. Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22.
23. Landy, M. S., & Graham, N. (2004). Visual perception of texture. In L. M. Chalupa & J. S. Werner (Eds.), The visual neurosciences. Chap. 2.
24. Leung, T., & Malik, J. (2001). Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision, 43 (1).
25. Li, Y., & Pollard, N. S. (2005). A shape matching algorithm for synthesizing humanlike enveloping grasps. In Humanoid robots.
26. Liu, T., Moore, A., Gray, A., & Yang, K. (2004). An investigation of practical approximate nearest neighbor algorithms. In Conference on neural information processing systems.
27. Lowe, D. G. (1999). Object recognition from local scale-invariant features. In IEEE international conference on computer vision.
28. Makadia, A., Patterson, A., & Daniilidis, K. (2006). Fully automatic registration of 3D point clouds. In IEEE conference on computer vision and pattern recognition.
29. Miller, A., & Allen, P. K. (2004). Graspit!: a versatile simulator for robotic grasping. IEEE Robotics and Automation Magazine, 11 (4).
30. Morales, A., Asfour, T., Azad, P., Knoop, S., & Dillmann, R. (2006). Integrated grasp planning and visual object localization for a humanoid robot with five-fingered hands. In IEEE international conference on intelligent robots and systems.
31. Novotni, M., & Klein, R. (2003). 3D Zernike descriptors for content based shape retrieval. In ACM symposium on solid modeling and applications.
32. Nowak, E., Jurie, F., & Triggs, B. (2006). Sampling strategies for bagof-features image classification. In European conference on computer vision.
33. Ohbuchi, R., Osada, K., Furuya, T., & Banno, T. (2008). Salient local visual featuers for shape-based 3D model retrieval. In IEEE international conference on shape modeling and applications.
34. Papadakis, P., Pratikakis, I., Theoharis, T., & Perantonis, S. (2010). PANORAMA: a 3D shape descriptor based on panoramic views for unsupervised 3d object retrieval. International Journal of Computer Vision, Special Issue on: 3D Object Retrieval, 89 (2).
35. Pelossof, R., Miller, A., Allen, P. K., & Jebara, T. (2004). An svm learning approach to robotic grasping. In IEEE international conference on robotics and automation.
36. Platt, R., Fagg, A. H., & Grupen, R. (2002). Nullspace composition of control laws for grasping. In IEEE international conference on robotics and automation.
37. Romea, A. C., & Srinivasa, S. (2010). Efficient multi-view object recognition and full pose estimation. In IEEE international conference on robotics and automation.
38. Rother, C., Kolmogorov, V., & Blake, A. (2004). Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics, 23.
39. Rusinkiewicz, S., & Levoy, M. (2001). Efficient variants of the icp algorithm. In IEEE international conference on 3-D digital imaging and modeling.
40. Salvi, J., Matabosch, C., Fofi, D., & Forest, J. (2007). A review of recent range image registration methods with accuracy evaluation. Image and Vision Computing, 25 (5).
41. Saxena, A., Driemeyer, J., & Ng, A. Y. (2008). Robotic grasping of novel objects using vision. International Journal of Robotics Research.
42. Shilane, P., Min, P., Kazhdan, M., & Funkhouser, T. (2004). The Princeton shape benchmark. In IEEE international conference on shape modeling and applications.
43. Sivamani, R. K., Goodman, J., Gitis, N. V., & Maibach, H. I. (2003). Coefficient of friction: tribological studies in man-an overview. Skin Research and Technology, 9 (3).
44. Srinivasa, S., Ferguson, D., Helfrich, C., Berenson, D., Romea, A. C., Diankov, R., Gallagher, G., Hollinger, G., Kuffner, J., & Vandeweghe, J. M. (2010). HERB: a home exploring robotic butler. Autonomous Robots, 28 (1).
45. Torres, M. M., Romea, A. C., & Srinivasa, S. (2010). Moped: a scalable and low latency object recognition and pose estimation system. In IEEE international conference on robotics and automation.
46. Triggs, B., McLauchlan, P. F., Hartley, R. I., & Fitzgibbon, A. W. (2000). Lecture notes in computer science: Vol. 1883. Bundle adjustment-a modern synthesis.