nach oben

Autonomous Robots

Erschienen in:

08.07.2019

Learning attentional regulations for structured tasks execution in robotic cognitive control

verfasst von: Riccardo Caccavale, Alberto Finzi

Erschienen in: Autonomous Robots | Ausgabe 8/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

We present a framework for robotic cognitive control endowed with adaptive mechanisms for attentional regulation and task execution. In cognitive psychology, cognitive control is the process that orchestrates executive and cognitive processes supporting adaptive responses and complex goal-directed behaviors. Similar mechanisms can be deployed in robotic systems in order to flexibly execute complex structured tasks. In this work, following a supervisory attentional system paradigm, we propose an approach that permits to learn how to exploit top-down and bottom-up attentional regulations to guide the execution of hierarchically structured tasks. We present the overall framework discussing its functioning in a mobile robot case study considering pick-carry-place tasks. In this setting, we show that the proposed system can be on-line trained by a user in order to execute incrementally complex activities.

Vorheriger Artikel A constrained instantaneous learning approach for aerial package delivery robots: onboard implementation and experimental results

Nächster Artikel Augmenting visual SLAM with Wi-Fi sensing for indoor applications

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Anderson, J. R., Matessa, M., & Lebiere, C. (1997). Act-r: A theory of higher level cognition and its relation to visual attention. Human-Computer Interaction, 12(4), 439–462.CrossRef

Belardinelli, A., Pirri, F., & Carbone, A. (2007). Bottom-up gaze shifts and fixations learning by imitation. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 37(2), 256–271.CrossRef

Borji, A., Ahmadabadi, M. N., Araabi, B. N., & Hamidi, M. (2010). Online learning of task-driven object-based visual attention control. Image and Vision Computing, 28(7), 1130–1145.CrossRef

Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624–652.CrossRef

Breazeal, C., Edsinger, A., Fitzpatrick, P., & Scassellati, B. (2001). Active vision for sociable robots. IEEE Transactions on Systems, Man and Cybernetics, Part A, 31(5), 443–453.CrossRef

Byrne, M. D. (2001). Act-r/pm and menu selection: Applying a cognitive architecture to hci. International Journal of Human-Computer Studies, 55(1), 41–84.CrossRef

Caccavale, R., & Finzi, A. (2015). Plan execution and attentional regulations for flexible human-robot interaction. In Proceedings of the IEEE international conference on systems, man, and cybernetics, pp 2453–2458.

Caccavale, R., & Finzi, A. (2016). Flexible task execution and attentional regulations in human–robot interaction. IEEE Transactions on Cognitive and Developmental Systems, 6(1), 68–79.CrossRef

Caccavale, R., Cacace, J., Fiore, M., Alami, R., & Finzi, A. (2016). Attentional supervision of human–robot collaborative plans. In: Proceedings of the IEEE international conference on robot and human interactive communication (RO-MAN) (pp 867–873). IEEE.

Caccavale, R., Saveriano, M., Finzi, A., & Lee, D. (2018). Kinesthetic teaching and attentional supervision of structured tasks in human–robot interaction. Autonomous Robots, pp 1–17.

Chernova, S., & Arkin, R. C. (2007). From deliberative to routine behaviors: A cognitively inspired action-selection mechanism for routine behavior capture. Adaptive Behavior, 15, 199–216.CrossRef

Colombini, E. L., da Silva, S. A., & Costa Ribeiro, C. H. (2017). An attentional model for autonomous mobile robots. IEEE Systems Journal, 11(3), 1308–1319.CrossRef

Cooper, R. P., & Shallice, T. (2000). Contention scheduling and the control of routine activities. Cognitive Neuropsychology, 17, 297–338.CrossRef

Cooper, R. P., & Shallice, T. (2006). Hierarchical schemas and goals in the control of sequential behavior. Psychological Review, 113(4), 887–916.CrossRef

Cox, B., & Krichmar, J. (2009). Neuromodulation as a robot controller. Robotics & Automation Magazine, 16(3), 72–80.CrossRef

Demiris, Y., & Khadhouri, B. (2006). Hierarchical attentive multiple models for execution and recognition of actions. Robotics and Autonomous Systems, 54(5), 361–369.CrossRef

Di Nocera, D., Finzi, A., Rossi, S., & Staffa, M. (2012). Attentional action selection using reinforcement learning. In Proceedings of the international conference on simulation of adaptive behavior (pp. 371–380). Springer.

Di Nocera, D., Finzi, A., Rossi, S., & Staffa, M. (2014). The role of intrinsic motivations in attention allocation and shifting. Frontiers in Psychology, 5, 273.CrossRef

Dong, D., & Franklin, S. (2015). Modeling sensorimotor learning in lida using a dynamic learning rate. Biologically Inspired Cognitive Architectures, 14, 1–9.CrossRef

Donnarumma, F., Prevete, R., Chersi, F., & Pezzulo, G. (2015a). A programmer-interpreter neural network architecture for prefrontal cognitive control. International Journal of Neural Systems, 25(6), 1550017. CrossRef

Donnarumma, F., Prevete, R., de Giorgio, A., Montone, G., & Pezzulo, G. (2015b). Learning programs is better than learning dynamics: A programmable neural network hierarchical architecture in a multi-task scenario. Adaptive Behavior, 24(1), 27–51.CrossRef

Franklin, S., Madl, T., & D’Mello, S. (2014). Lida: A systems-level architecture for cognition, emotion, and learning. IEEE Transactions on Autonomous Mental Development, 6(1), 19–41.CrossRef

Garcez, A., Besold, T. R., De Raedt, L., Földiak, P., Hitzler, P., Icard, T., Kühnberger, K. U., Lamb, L. C., Miikkulainen, R., & Silver, D. L. (2015). Neural-symbolic learning and reasoning: Contributions and challenges. In Proceedings of the AAAI spring symposium on knowledge representation and reasoning: Integrating symbolic and neural approaches, Stanford.

Garcez, A. S., Lamb, L. C., & Gabbay, D. M. (2008). Neural-symbolic cognitive reasoning. Berlin: Springer. MATH

Garforth, J., McHale, S. L., & Meehan, A. (2006). Executive attention, task selection and attention-based learning in a neurally controlled simulated robot. Neurocomputing, 69(16–18), 1923–1945.CrossRef

Gianni, M., Kruijff, G. J. M., & Pirri, F. (2015). A stimulus-response framework for robot control. ACM Transactions on Interactive Intelligent Systems, 4(4), 21:1–21:41.CrossRef

Kasderidis, S., & Taylor, J. (2004). Attentional agents and robot control. International Journal of Knowledge-Based and Intelligent Engineering Systems, 8(2), 69–89.CrossRef

Kawamura, K., Gordon, S. M., Ratanaswasd, P., Erdemir, E., & Hall, J. F. (2008). Implementation of cognitive control for a humanoid robot. International Journal of Humanoid Robotics, 5(04), 547–586.CrossRef

Khamassi, M., Lallée, S., Enel, P., Procyk, E., & Dominey, P. F. (2011). Robot cognitive control with a neurophysiologically inspired reinforcement learning model. Frontiers in NeuroRobotics, 5(1).

Laird, J. E., Newell, A., & Rosenbloom, P. S. (1987). Soar: An architecture for general intelligence. Artificial Intelligence, 33(1), 1–64.CrossRef

Lashley, K. S. (1951). The problem of serial order in behavior. In L. A. Jeffress (Ed.), Cerebral mechanisms in behavior. New York, NY: Wiley.

Menna, M., Gianni, M., & Pirri, F. (2013). Learning the dynamic process of inhibition and task switching in robotics cognitive control. In Proceedings of ICMLA 2013, Vol. 1, pp. 392–397.

Mozer, M. C., & Sitton, M. (1998). Computational modeling of spatial attention. Attention, 9, 341–393.

Nagai, Y. (2009). From bottom-up visual attention to robot action learning. In Proceedings of international conference on development and learning, pp 1–6.

Nau, D., Cao, Y., Lotem, A., & Muñoz-Avila, H. (1999). Shop: Simple hierarchical ordered planner. In Proceedings of IJCAI (pp. 968–973). Morgan Kaufmann Publishers Inc.

Nicolescu, M. N., & Mataric, M. J. (2003). Natural methods for robot task learning: Instructive demonstrations, generalization and practice. In Proceedings of AAMAS (pp. 241–248). ACM.

Norman, D. A., & Shallice, T. (1986). Attention to action: Willed and automatic control of behavior. In R. J. Davidson, G. E. Schwartz and D. Shapiro (Eds.), Consciousness and self-regulation: Advances in research (Vol. IV, Chap 1, pp. 1–18). New York, NY: Plenum Press.

Pardowitz, M., Knoop, S., Dillmann, R., & Zollner, R. D. (2007). Incremental learning of tasks from user demonstrations, past experiences, and vocal comments. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 37(2), 322–332.CrossRef

Posner, M. I., & Snyder, C. R. R. (1975). Attention and cognitive control. In: Information processing and cognition, pp. 55–85.

Rubinstein, J., Meyer, E., & Evan, J. E. (2001). Executive control of cognitive processes in task switching. Journal of Experimental Psychology: Human Perception and Performance, 27(4), 763–797.

Titel: Learning attentional regulations for structured tasks execution in robotic cognitive control
verfasst von: Riccardo Caccavale
Alberto Finzi
Publikationsdatum: 08.07.2019
Verlag: Springer US
Erschienen in: Autonomous Robots / Ausgabe 8/2019
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI: https://doi.org/10.1007/s10514-019-09876-x

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 8/2019

Motion encoding with asynchronous trajectories of repetitive teleoperation tasks and its extension to human-agent shared teleoperation

Real-time robot path planning from simple to complex obstacle patterns via transfer learning of options

Distributed iterative learning control for multi-agent systems

NP-completeness of optimal planning problem for modular robots

Horizon-based lazy optimal RRT for fast, efficient replanning in dynamic environment

The impact of load on the wheel rolling radius and slip in a small mobile platform