Learning to place new objects in a scene

International Journal of Robotics Research

Volumn 31, Issue 9, 2012, Pages 1021-1043

  Jiang, Yun ^a   Lim, Marcus ^a   Zheng, Changxi ^a   Saxena, Ashutosh ^a  

^a Department of Computer Science and School of Operations Research and Information Engineering   (United States)

Author keywords

Cognitive robotics; Learning and adaptive systems; Manipulation; Range sensing; Sensing and perception computer vision

Indexed keywords

COGNITIVE ROBOTICS; LEARNING AND ADAPTIVE SYSTEM; MANIPULATION; RANGE SENSING; SENSING AND PERCEPTION;

ADAPTIVE SYSTEMS; INTEGER PROGRAMMING; LOADING; ROBOTICS; ROBOTS;

COMPUTER VISION;

EID: 84864475487     PISSN: 02783649     EISSN: 17413176     Source Type: Journal    
DOI: 10.1177/0278364912438781     Document Type: Article

References (83)

1. Koppula HS, Anand A, Joachims T, Saxena A. Semantic labeling of 3D point clouds for indoor scenes. 25th annual conference on neural information processing systems ; 2011 Granada, Spain ; 2011..
2. Berenson D, Diankov R, Nishiwaki K, Kagami S, Kuffner J. Grasp planning in complex scenes. 7th international IEEE-RAS conference on humanoid robots ; 2007 Pittsburgh, USA ; 2007. 42.
3. Berg A, Berg T, Malik J. Shape matching and object recognition using low distortion correspondences. ; 2005 San Diego, USA ; 2005. 26.
4. BerkelaarMEiklandKNotebaertP (2004) LP-solve 5.5, open source (mixed-integer) linear programming system. http://lpsolve.sourceforge.net/5.5/.
5. Bicchi A, Kumar V. Robotic grasping and contact: a review. IEEE international conference on robotics and automation ; 2000 San Francisco, USA ; 2000. 348.
6. Brook P, Ciocarlie M, Hsiao K. Collaborative grasp planning with multiple object representations. IEEE international conference on robotics and automation ; 2011 Shanghai, China ; 2011. 2851.
7. Ciocarlie MT, Allen PK. On-line interactive dexterous grasping. ; 2008 Madrid, Spain ; 2008. 104.
8. Coffman E, Garey M, Johnson D Approximation algorithms for NP-hard problems. Hochbaum DS, ed. Boston: PWS Publishing Company ; 1996: 46-93.
9. Cortes C, Vapnik V. Support-vector networks. Machine learning. 1995 ; 20 (3). 273-297
10. Deng J, Berg A, Fei-Fei L. Hierarchical semantic indexing for large scale image retrieval. IEEE computer society conference on computer vision and pattern recognition ; 2011 Colorado Springs, USA ; 2011. 785.
11. Diankov R, Kuffner J Pittsburgh, USA: Report, Robotics Institute ; 2008 :
12. Diankov R, Srinivasa S, Ferguson D, Kuffner J. Manipulation planning with caging grasps. 8th international IEEE-RAS conference on humanoid robots ; 2008 Daejeon, South Korea ; 2008. 285.
13. Divvala S, Hoiem D, Hays J, Efros A, Hebert M. An empirical study of context in object detection. ; 2009 Miami, USA ; 2009. 1271.
14. Dogar M, Srinivasa S. A framework for push-grasping in clutter. Robotics: science and systems. 2011 2011 ;:
15. Dy J, Brodley C. Feature selection for unsupervised learning. Journal of Machine Learning Research. 2004 ; 5: 845-889
16. Edsinger A, Kemp C. Manipulation in human environments. ; 2006 Genoa, Italy ; 2006. 102.
17. Fergus R, Perona P, Zisserman A. Object class recognition by unsupervised scale-invariant learning. IEEE computer society conference on computer vision and pattern recognition ; 2003 Wisconsin, USA ; 2003. 264.
18. FergusonM (2011) Improved AR markers for topological navigation. ROS 3D Contest. http://www.ros.org/news/2011/01/ros-3d-entries-improved-ar-markers-for- topological-navigation.html.
19. Fisher M. The Lagrangian relaxation method for solving integer programming problems. Management Science. 1981 ; 27: 1-18 (Pubitemid 11481441)
20. Fisher M, Hanrahan P. Context-based search for 3D models. ACM Transactions on Graphics. 2010 ; 29 (6). 182
21. Fisher M, Savva M, Hanrahan P. Characterizing structural relationships in scenes using graph kernels. ACM Transactions on Graphics. 2011 ; 30 (4). 34
22. Fu H, Cohen-Or D, Dror G, Sheffer A. Upright orientation of man-made objects. ACM Transactions on Graphics. 2008 ; 27 (3). 42
23. Gelfand N, Mitra NJ, Guibas LJ, Pottmann H. Robust global registration. 3rd eurographics symposium on geometry processing ; 2005 Vienna, Austria 4-6 July 2005 ; 2005..
24. Globerson A, Jaakkola T. Fixing max-product: convergent message passing algorithms for map LP-relaxations. 21st annual conference on neural information processing systems ; 2007 Vancouver, Canada ; 2007..
25. Glover J, Rusu R, Bradski G. Monte carlo pose estimation with quaternion kernels and the Bingham distribution. Robotics: science and systems. 2011 2011 ;:
26. Hanley J, McNeil B. The meaning and use of the area under a receiver operating (ROC) curvel characteristic. Radiology. 1982 ; 143 (1). 29-36 (Pubitemid 12142173)
27. Hedau V, Hoiem D, Forsyth D. Recovering the spatial layout of cluttered rooms. 12th international IEEE conference on computer vision ; 2009 Kyoto, Japan ; 2009. 1849.
28. Heitz G, Gould S, Saxena A, Koller D. Cascaded classification models: combining models for holistic scene understanding. 22nd annual conference on neural information processing systems ; 2008 Vancouver, Canada ; 2008..
29. Ho ESL, Komura T, Tai CL. Spatial relationship preserving character motion adaptation. ACM Transactions on Graphics. 2010 ; 29 (4). 33
30. Hsiao K, Nangeroni P, Huber M, Saxena A, Ng AY. Reactive grasping using optical proximity sensors. IEEE international conference on robotics and automation ; 2009 Kobe, Japan ; 2009. 4230.
31. Jain A, Kemp C. Pulling open doors and drawers: coordinating an omni-directional base and a compliant arm with equilibrium point control. IEEE international conference on robotics and automation ; 2010 Anchorage, USA ; 2010. 1807.
32. Jalali A, Ravikumar P, Sanghavi S, Ruan C. A dirty model for multi-task learning. 24th annual conference on neural information processing systems ; 2010 Vancouver, Canada ; 2010..
33. Jetchev N, Toussaint M. Task space retrieval using inverse feedback control. 28th international conference on machine learning ; 2011 Washington, USA ; 2011..
34. Jiang Y, Amend J, Lipson H, Saxena A. Learning hardware agnostic grasps for a universal jamming gripper. IEEE international conference on robotics and automation ; 2012a Minnesota, USA ; 2012a..
35. Jiang Y, Moseson S, Saxena A.. Efficient grasping from RGBD images: learning using a new rectangle representation ; 2011a Shanghai, China ; 2011a..
36. Jiang Y, Zheng C, Lim M, Saxena A. Learning to place new objects. ; 2011b Los Angeles, USA ; 2011b..
37. Jiang Y, Zheng C, Lim M, Saxena A. Learning to place new objects. IEEE international conference on robotics and automation ; 2012b Minnesota, USA ; 2012b..
38. Joachims T Making large-scale SVM learning practical. Cambridge: MIT Press ; 1999 :
39. Johnson A, Hebert M. Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1999 ; 21 (5). 433-449
40. Katz D, Brock O. Manipulating articulated objects with interactive perception. IEEE international conference on robotics and automation ; 2008 Pasadena, USA ; 2008. 272.
41. Klingbeil E, Saxena A, Ng AY. Learning to open new doors. ; 2010 Taipei, Taiwan ; 2010. 2751.
42. Koller D, Friedman N Probabilistic graphical models: principles and techniques. Cambridge: MIT Press ; 2009 :
43. Lai K, Bo L, Ren X, Fox D. Sparse distance learning for object recognition combining RGB and depth information. IEEE international conference on robotics and automation ; 2011 Shanghai, China ; 2011..
44. Le QV, Kamm D, Kara A, Ng AY. Learning to grasp objects with multiple contact points. IEEE international conference on robotics and automation ; 2010 Anchorage, USA ; 2010. 5062.
45. Leibe B, Leonardis A, Schiele B. Combined object categorization and segmentation with an implicit shape model. Workshop on statistical learning in computer vision ; 2004 Prague, Czech Republic ; 2004..
46. Li C, Kowdle A, Saxena A, Chen T. Towards holistic scene understanding: Feedback enabled cascaded classification models. 24th annual conference on neural information processing systems ; 2010 Vancouver, Canada ; 2010..
47. Li C, Saxena A, Chen T.-mrf: capturing spatial and semantic structure in the parameters for scene understanding. 25th annual conference on neural information processing systems ; 2011 Granada, Spain ; 2011..
48. Liebelt J, Schmid C, Schertler K. Viewpoint-independent object class detection using 3D feature maps. IEEE computer society conference on computer vision and pattern recognition ; 2008 Anchorage, USA ; 2008. 1.
49. Liu H, Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering. 2005 ; 17 (4). 491-502 (Pubitemid 40495592)
50. Lodi A, Martello S, Vigo D. Heuristic algorithms for the three-dimensional bin packing problem. European Journal of Operational Research. 2002 ; 141 (2). 410-420 (Pubitemid 34623946)
51. Lozano-Pérez T, Jones J, Mazer E, O'Donnell P. Task-level planning of pick-and-place robot motions. Computer. 2002 ; 22 (3). 21-29
52. Maitin-Shepard J, Cusumano-Towner M, Lei J, Abbeel P. Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding. IEEE international conference on robotics and automation ; 2010 Anchorage, USA ; 2010. 2308.
53. Miller AT, Knoop S, Christensen HI, Allen PK. Automatic grasp planning using shape primitives. IEEE international conference on robotics and automation ; 2003 Taipei, Taiwan ; 2003. 1824.
54. Nguyen V. Constructing stable force-closure grasps. ; 1986 Dallas, USA ; 1986. 129.
55. Novotni M, Klein R. 3D Zernike descriptors for content based shape retrieval. 8th ACM symposium on solid modeling and applications ; 2003 Seattle, USA ; 2003. 216.
56. Pisinger D, Sigurd M. Using decomposition techniques and constraint programming for solving the two-dimensional bin-packing problem. INFORMS Journal on Computing. 2007 ; 19 (1). 36-51
57. Ponce J, Stam D, Faverjon B. On computing two-finger force-closure grasps of curved 2D objects. International Journal of Robotics Research. 1993 ; 12 (3). 263
58. Quigley M, Gerkey B, Conley K, Faust J, Foote T, Leibs J, Berger E, Wheeler R, Ng A. ROS: an open-source robot operating system. Workshop on open source software in robotics ; 2009 Kobe, Japan ; 2009..
59. Rao D, Le Q, Phoka T, Quigley M, Sudsang A, Ng A. Grasping novel objects with depth segmentation. IEEE-RSJ international conference on intelligent robots and systems ; 2010 Taipei, Taiwan ; 2010. 2578.
60. Rosales C, Porta J, Ros L Robotics: science and systems. USA Cambridge: Los Angeles MIT Press ; 2011 2011: 1-8.
61. Roth D, Yih W. Integer linear programming inference for conditional random fields. ; 2005 Bonn, Germany ; 2005. 736.
62. Rusinkiewicz S, Levoy M. Efficient variants of the ICP algorithm. 3rd international conference on 3D digital imaging and modeling ; 2001 Quebec City, Canada ; 2001. 145.
63. Rusu R, Marton Z, Blodow N, Beetz M. Learning informative point classes for the acquisition of object model maps. 10th international conference on control, automation, robotics and vision ; 2008 Hanoi, Vietnam ; 2008. 643.
64. Rusu RB, Blodow N, Beetz M. Fast point feature histograms (FPFH) for 3D registration. IEEE international conference on robotics and automation ; 2009 Kobe, Japan ; 2009. 3212.
65. Rusu RB, Cousins S. 3D is here: point cloud library (PCL). IEEE international conference on robotics and automation ; 2011 Shanghai, China ; 2011. 1.
66. Savarese S, Fei-Fei L. 3D generic object categorization, localization and pose estimation. 11th IEEE international conference on computer vision ; 2007 Rio de Janeiro, Brazil ; 2007. 1.
67. Saxena A, Chung SH, Ng AY. 3-D depth reconstruction from a single still image. International Journal of Computer Vision. 2008a ; 76 (1). 53-69 (Pubitemid 350234087)
68. Saxena A, Driemeyer J, Kearns J, Ng AY. Robotic grasping of novel objects. 21st annual conference on neural information processing systems ; 2006 Vancouver, Canada 3-5 December 2007 ; 2006..
69. Saxena A, Driemeyer J, Ng A. Robotic grasping of novel objects using vision. International Journal of Robotics Research. 2008b ; 27 (2). 157
70. Saxena A, Driemeyer J, Ng AY. Learning 3-D object orientation from images. IEEE international conference on robotics and automation ; 2009a Kobe, Japan ; 2009a. 4266.
71. Saxena A, Sun M, Ng A. Make3D: Learning 3D scene structure from a single still image. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2009b ; 31 (5). 824-840
72. Saxena A, Wong L, Ng AY. Learning grasp strategies with partial shape information. 23rd conference on artificial intelligence ; 2008c Illinois, USA ; 2008c. 1491.
73. Schuster M, Okerman J, Nguyen H, Rehg J, Kemp C. Perceiving clutter and surfaces for object placement in indoor environments. 10th international IEEE-RAS conference on humanoid robots ; 2010 Nashville, USA ; 2010. 152.
74. Sturm J, Konolige K, Stachniss C, Burgard W. 3D pose estimation, tracking and model learning of articulated objects from dense depth video using projected texture stereo. Robotics: science and systems. 2010 2010 ;:
75. Sugie H, Inagaki Y, Ono S, Aisu H, Unemi T. Placing objects with multiple mobile robots-mutual help using intention inference. IEEE international conference on robotics and automation ; 1995 Nagoya, Japan ; 1995. 2181.
76. Taskar B, Chatalbashev V, Koller D. Learning associative Markov networks. ; 2004 Banff, Canada ; 2004. 102.
77. Taskar B, Guestrin C, Koller D. Max-margin Markov networks. 17th annual conference on neural information processing systems ; 2003 Vancouver, Canada ; 2003..
78. Thomas A, Ferrar V, Leibe B, Tuytelaars T, Schiel B, Van Gool L. Towards multi-view object class detection. ; 2006 New York, USA ; 2006. 1589.
79. Torralba A, Murphy K, Freeman WT. Using the forest to see the trees: object recognition in context. Communications of the ACM. 2010 ; 53 (3). 107-114
80. Toussaint M, Plath N, Lang T, Jetchev N. Integrated motor control, planning, grasping and high-level reasoning in a blocks world using probabilistic inference. IEEE international conference on robotics and automation ; 2010 Anchorage, USA ; 2010. 385.
81. Winn J, Criminisi A, Minka T. Object categorization by learned universal visual dictionary. 10th IEEE international conference on computer vision ; 2005 Beijing, China ; 2005. 1800.
82. Xiong X, Huber D. Using context to create semantic 3D models of indoor environments. 21st British machine vision conference ; 2010 Aberystwyth, Wales ; 2010..
83. Yanover C, Meltzer T, Weiss Y. Linear programming relaxations and belief propagation-an empirical study. Journal of Machine Learning Research. 2006 ; 7: 1887-1907 (Pubitemid 44423552)