Towards Self-controlled Robots Through Distributed Adaptive Control

详细信息    查看全文

• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9793
• 期：1
• 页码：490-497
• 全文大小：1,267 KB
• 参考文献：1.Anderson, J.R., Matessa, M., Lebiere, C.: ACT-R: a theory of higher level cognition and its relation to visual attention. Hum.-Comput. Interact. 12(4), 439–462 (1997)CrossRef
  2.Laird, J.E., Newell, A., Rosenbloom, P.S.: Soar: an architecture for general intelligence. Artif. Intell. 33(1), 1–64 (1987)MathSciNet CrossRef
  3.Lallee, S., et al.: Towards the synthetic self: making others perceive me as an other. Paladyn, J. Behav. Robot. 6(1) (2015)
  4.Marcos, E., Ringwald, M., Duff, Y., Sánchez-Fibla, M., Verschure, P.F.M.J.: The hierarchical accumulation of knowledge in the distributed adaptive control architecture. In: Baldassarre, G., Mirolli, M. (eds.) Computational and Robotic Models of the Hierarchical Organization of Behavior, pp. 213–234. Springer, Heidelberg (2013)
  5.Metta, G., Fitzpatrick, P., Natale, L.: YARP: yet another robot platform. Int. J. Adv. Robot. Syst. 3(1), 43–48 (2006)
  6.Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)CrossRef
  7.Petit, M., Fischer, T., Demiris, Y.: Lifelong augmentation of multi-modal streaming autobiographical memories. IEEE Trans. Cogn. Dev. Syst. 8(3), 201–213 (2016)CrossRef
  8.Puigbò, J.-Y., Herreros, I., Moulin-Frier, C., Verschure, P.F.M.J.: Towards a two-phase model of sensor and motor learning. In: Wilson, S.P., Verschure, P.F.M.J., Mura, A., Prescott, T.J. (eds.) Living Machines 2015. LNCS, vol. 9222, pp. 453–460. Springer, Heidelberg (2015)CrossRef
  9.Puigbò, J.-Y., et al.: Using a cognitive architecture for general purpose service robot control. Connection Sci. 27(2), 105–117 (2015)CrossRef
  10.Sanchez-Fibla, M., et al.: Allostatic control for robot behavior regulation: a comparative rodent-robot study. Adv. Complex Syst. 13(03), 377–403 (2010)MathSciNet CrossRef MATH
  11.Verschure, P.F., Pennartz, C.M., Pezzulo, G.: The why, what, where, when and how of goal-directed choice: neuronal and computational principles. Phil. Trans. R. Soc. B 369(1655) (2014)
  
• 作者单位：Jordi-Ysard Puigbò (19)
  Clément Moulin-Frier (19)
  Paul F. M. J. Verschure (19) (20)
  
  19. Laboratory of Synthetic, Perceptive, Emotive and Cognitive Science (SPECS), DTIC, Universitat Pompeu Fabra (UPF), Barcelona, Spain
  20. Catalan Research Institute and Advanced Studies (ICREA), Barcelona, Spain
  
• 丛书名：Biomimetic and Biohybrid Systems
• ISBN：978-3-319-42417-0
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9793

文摘

Robots, as well as machine learning algorithms, have proven to be, unlike human beings, very sensitive to errors and failure. Artificial intelligence and machine learning are nowadays the main source of algorithms that drive cognitive robotics research. The advances in the fields have been huge during the last year, beating expert-human performance in video games, an achievement that was unthinkable a few years ago. Still, performance has been assessed by external measures not necessarily fit to the problem to solve, what lead to shameful failure on some specific tasks. We propose that the way to achieve human-like robustness in performance is to consider the self of the agent as the real source of self-evaluated error. This offers a solution to acting when information or resources are scarce and learning speed is important. This paper details our extension of the cognitive architecture DAC to control embodied agents and robots, through self-generated signals, from needs, drives, self-generated value and goals.