Learning visual representations for perception-action systems

International Journal of Robotics Research

Volumn 30, Issue 3, 2011, Pages 294-307

  Piater, Justus ^a   Jodogne, Sébastien ^b   Detry, Renaud ^a   Kraft, Dirk ^c   Krüger, Norbert ^c   Kroemer, Oliver ^d   Peters, Jan ^d  

^a UNIVERSITY OF LIÈGE   (Belgium)

^b EURESYS Sa   (Belgium)

^c UNIVERSITY OF SOUTHERN DENMARK   (Denmark)

^d MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

Autonomous agents; cognitive robotics; computer vision; grasping; learning and adaptive systems; manipulation; sensing and perception

Indexed keywords

COGNITIVE ROBOTICS; GRASPING; LEARNING AND ADAPTIVE SYSTEMS; MANIPULATION; SENSING AND PERCEPTION;

ADAPTIVE SYSTEMS; ALGORITHMS; COGNITIVE SYSTEMS; COMPUTER VISION; FUZZY CLUSTERING; OBJECT RECOGNITION; REINFORCEMENT LEARNING; ROBOTICS;

AUTONOMOUS AGENTS;

EID: 79952136450     PISSN: 02783649     EISSN: 17413176     Source Type: Journal    
DOI: 10.1177/0278364910382464     Document Type: Conference Paper

References (47)

1. Bagnell, J. and Schneider, J. (2001). Autonomous Helicopter Control using Reinforcement Learning Policy Search Methods. International Conference on Robotics and Automation.
2. Bear M., Connors B. and Paradiso M. (2006). Neuroscience: Exploring the Brain. Lippincott Williams & Wilkins.
3. Bellman, R. (1957). Dynamic Programming. Princeton University Press.
4. Bertsekas, D. and Tsitsiklis, J. (1996). Neuro-Dynamic Programming. Athena Scientific.
5. Braitenberg, V. (1984). Vehicles: Experiments in synthetic psychology. Cambridge, MA, USA, MIT Press.
6. Breiman, L. and Friedman, J. and Stone, C. (1984). Classification and Regression Trees. Wadsworth International Group.
7. Brooks, R. (1991). Intelligence without representation. Artificial Intelligence 47: 139-159.
8. Bryant, R. (1992). Symbolic (Pubitemid 23599609)
9. Detry, R., Başeski, E., Popović, M., Touati, Y., Krüger, N., Kroemer, O., Peters, J., and Piater, J. (2009 a). Learning objectspecific grasp affordance densities. International Conference on Development and Learning.
10. Detry, R., Kraft, D., Buch, A., Krüger, N., and Piater, J. (2010). Refining grasp affordance models by experience. International Conference on Robotics and Automation, pp. 2287-2293.
11. Detry, R., Pugeault, N., and Piater, J. (2009 b). A probabilistic framework for 3d visual object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (10). 1790-1803.
12. Durrant-Whyte, H. and Bailey, T. (2006). Simultaneous Localisation and Mapping (SLAM): Part I The essential algorithms. Robotics and Automation Magazine 13: 99-110.
13. Ernst, D. Geurts, P. and Wehenkel, L. (2003). Iteratively extending time horizon reinforcement learning. 14th European Conference on Machine Learning, pp. 96-107.
14. Geman, S. and Bienenstock, E. and Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation 4 (1). 1-58.
15. Gibson, J. (1979). The Ecological Approach to Visual Perception. Boston, MA, Houghton Mifflin.
16. Gouet, V. and Boujemaa, N., (2001). Object-based queries using color points of interest. IEEE Workshop on Content-Based Access of Image and Video Libraries, pp. 30-36.
17. Hübner, K. and Kragić, D. (2008). Selection of robot pre-grasps using box-based shape approximation. IEEE/RSJ International Conference on Intelligent Robots and Systems.
18. Jodogne, S. and Piater, J. (2005 a). Interactive learning of mappings from visual percepts to actions. 22nd International Conference on Machine Lea-rning, pp. 393-400.
19. Jodogne, S. and Piater, J. (2005 b). Learning, then compacting visual policies. 7th European Workshop on Reinforcement Learning, pp. 8-10.
20. Jodogne, S. and Piater, J. (2006). Task-driven discretization of the joint space of visual percepts and continuous actions. European Conference on Machine Learning (Lecture Notes in Computer Science, vol. 4212). Springer, pp. 222-233. (Pubitemid 44618834)
21. Jodogne, S. and Piater, J. (2007). Closed-loop learning of visual control policies. Journal of Artificial Intelligence Research 28: 349-391. (Pubitemid 46790305)
22. Jodogne, S., Scalzo, F., and Piater, J. (2005). Task-driven learning of spatial combinations of visual features. Proceedings of the IEEE Workshop on Learning in Computer Vision and Pattern Recognition.
23. Kaelbling, L., Littman, M. and Cassandra, A. (1998). Planning and acting in partially observable stochastic domains. Artificial Intelligence 101: 99-134. (Pubitemid 128387390)
24. Kellman, P. and Arterberry, M. (1998). The Cradle of Knowledge. Cambridge, MA, MIT Press.
25. Kolter, J. and Ng, Andrew (2009). Policy search via the signed derivative. Cambridge, USA: MIT Press. Robotics: Science and Systems.
26. Kraft, D., Detry, R., Pugeault, N., Başeski, E., Piater, J., and Krüger, N. (2009). Learning objects and grasp affordances through autonomous exploration. Computer Vision Systems: Seventh International Conference (Lecture Notes in Computer Science), vol. 5815. Springer, pp. 235-244.
27. Kraft, D., Pugeault, N., Başeski, E., Popović, M., Kragić, D., Kalkan, S., Wörgötter, F., Krüger, N. (2008). Birth of the object: detection of objectness and extraction of object shape through object action complexes. International Journal of Humanoid Robotics 5: 247-265.
28. Krüger, N., Lappe, M. and Wörgötter, F. (2004). Biologically motivated multimodal processing of visual primitives. Interdisciplinary Journal of Artificial Intelligence and the Simulation of Behaviour 1 (5). 417-428.
29. Miller, A., Knoop, S., Christensen, H., and Allen, P. (2003). Automatic grasp planning using shape primitives. IEEE International Conference on Robotics and Automation, 1824-1829.
30. Milner, A. and Goodale, M. (1995). The Visual Brain in Action. Oxford University Press.
31. Montesano, L., Lopes, M., Bernardino, A., Santos-Victor, J. (2008). Learning object affordances: from sensory motor maps to imitation. IEEE Transactions on Robotics 24 (1). 15-26. (Pubitemid 351411194)
32. Moore, A., Atkeson, C. (1995). The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces. emphMachine Learning 21 (3). 199-233.
33. Nene, S. and Nayar, Shree and Murase, H. (1996). Columbia Object Image Library (COIL-100). Technical Report CUCS-006-96, Columbia University, New York.
34. Peng, J. and Williams, R. (1993). Efficient learning and planning with the DYNA framework. Adaptive Behavior 1: 437-454.
35. Peters, J., Schaal, S. (2008). Reinforcement learning of motor skills with policy gradients. Neural Networks 21 (4). 682-697.
36. Popović, M., Kraft, D., Bodenhagen, L., Başeski, E., Pugeault, N., Kragić, D., Asfour, T. and Krüger, N. (2010). A strategy for grasping unknown objects based on co-planarity and colour information. Robotics and Autonomous Systems 58 (5). 551-565.
37. Pugeault, N., Wörgötter, F. and Krüger, Norbert (2008). Accumulated visual representation for cognitive vision. British Machine Vision Conference.
38. Pugeault, N., Wörgötter, F. and Krüger, Norbert (2010). Visual primitives: local, condensed, and semantically rich visual descriptors and their applications in robotics. International Journal of Humanoid Robotics, accepted.
39. Riedmiller, M., Gabel, T., Hafner, R. and Lange, S. (2009). Reinforcement learning for robot soccer. Autonomous Robots 27 (1). 55-74.
40. Saxena, A., Driemeyer, J. and Ng, Andrew (2008). Robotic grasping of novel objects using vision. International Journal of Robotics Research 27 (2). 157-173. (Pubitemid 351169712)
41. Schaal, S. (1997). Learning from demonstration. Advances in Neural Information Processing Systems, volume 9. MIT Press.
42. Shimoga, K., (1996). Robot grasp synthesis algorithms: A survey. International Journal of Robotics Research 15 (3). 230-266. (Pubitemid 126547151)
43. Sudderth, E., Ihler, A., Freeman, W. and Willsky, Alan (2003). Nonparametric belief propagation. Computer Vision and Pattern Recognition, volume I, 605-612.
44. Sutton, R. and Barto, A. (1998). Reinforcement Learning: An Introduction. Cambridge, MA, MIT Press.
45. Thrun, S., Burgard, W., and Fox, D. (2005). Probabilistic Robotics. Cambridge, MA, MIT Press.
46. Ullman, S. (1984). Visual routines. Cognition 18: 97-159.
47. Watkins, Christopher (1989). Learning From Delayed Rewards. Ph.D. Thesis, King's College, Cambridge, UK.