From primitive behaviors to goal-directed behavior using affordances

IEEE International Conference on Intelligent Robots and Systems

Volumn , Issue , 2007, Pages 729-734

  Doǧar, Mehmet R ^a   Çakmak, Maya ^a   Uǧur, Emre ^a   Şahin, Erol ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

INTELLIGENT ROBOTS; INTELLIGENT SYSTEMS;

3D LASER SCANNERS; GOAL-DIRECTED BEHAVIOR; INTERNATIONAL CONFERENCES;

ROBOTS;

EID: 40949101761     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2007.4399469     Document Type: Conference Paper

References (17)

1. J.J. Gibson. The Ecological Approach to Visual Perception. Lawrence Erlbaum Associates, 1986. Originally published in 1979.
2. E.J. Gibson. Perceptual learning in development: Some basic concepts. Ecological Psychology, 12(4):295-302, 2000.
3. E.J. Gibson. The world is so full of a number of things:on specification and perceptual learning. Ecological Psy., 15(4):283 - 288, 2003.
4. R.R. Murphy. Case studies of applying gibson's ecological approach to mobile robots. IEEE Transactions on Systems, Man, and Cybernetics, 29(1):105-111, 1999.
5. M. Lungarella, G. Metta, R. Pfeifer, and G. Sandini. Developmental robotics: a survey. Connection Science, 15(4): 151 - 190, 2003.
6. P. Fitzpatrick, G. Metta, L. Natale, A. Rao, and G. Sandini. Learning about objects through action-initial steps towards artificial cognition. In Proc. of ICRA'03, pages 3140-3145, 2003.
7. K.F. MacDorman. Responding to affordances: Learning and projecting a sensorimotor mapping. In Proc. of ICRA '00, pages 3253-3259, 2000.
8. A. Stoytchev. Behavior-grounded representation of tool affordances. In Proc. of ICRA'05, pages 18-22, 2005.
9. I. Cos-Aguilera, L. Canamero, and G.M. Hayes. Motivation-driven learning of object affordances: First experiments using a simulated Khepera robot. In Proc. of 9. Int. Conf. in Cognitive Modelling, 2003.
10. G. Fritz, L. Paletta, M. Kumar, G. Dorffner, R. Breithaupt, and E. Rome. Visual learning of affordance based cues. In S. Nolfi, G. Baldassarre, R. Calabretta, J. Hallam, D. Marocco, J-A. Meyer, and D. Parisi, editors, From animals to animats 9: Proc. of the 9. Int. Conf. on SAB, LNAI. Volume 4095., pages 52-64, 2006.
11. D. Kim, J. Sun, S. M. Oh, J. M. Rehg, and A. Bobick. Traversability classification using unsupervised on-line visual learning for outdoor robot navigation. In Proc. of ICRA '06, 2006.
12. E. Uǧur, M.R. Doǧar, M. Çakmak, and E. Şahin. The learning and use of traversability affordance using range images on a mobile robot. In Proc. of ICRA'07, 2007.
13. E. Uǧur, M.R. Doǧar, M. Çakmak, and E. Şahin, Curiosity-driven learning of traversability affordance on a mobile robot. In Proc. of ICDL'07, 2007.
14. E. Şahin, M. Çakmak, M. R. Doǧar, E. Uǧur, and G. Üçoluk. To afford or not to afford: A new formalization of affordances towards affordance-based robot control. Adaptive Behavior, 2007. (in press).
15. E. Uǧur, M.R. Doǧar, O. Soysal, M. Çakmak, and E. Şahin. MACSim: Physics-based simulation of the KURT3D robot platform for studying affordances, 2006. MACS Project Deliverable 1.2.1.
16. I.H. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco, 2nd edition edition, 2005.
17. C. Chang and C. Lin. LIBSVM: a library for support vector machines, 2001. http://www.csie.ntu.edu.tw/∼cjlin/libsvm.