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Autonomous Robots

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07-06-2019

ART neural network-based integration of episodic memory and semantic memory for task planning for robots

Authors: Jauwairia Nasir, Deok-Hwa Kim, Jong-Hwan Kim

Published in: Autonomous Robots | Issue 8/2019

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Abstract

Automated task planning for robots faces great challenges in that the sequences of events needed for a particular task are mostly required to be hard-coded. This can be a cumbersome process, especially, when the user wants a robot to learn a large number of similar tasks with different objects that are semantically related. We propose a novel approach of user preference-based integrated multi-memory model (pMM-ART). This approach focuses on exploiting a semantic hierarchy of objects alongside an episodic memory for enhancing the behavior of an autonomous agent. We analyze the functioning principle of the proposed model by teaching it a few distinct domestic tasks and observe that it is able to carry out a large number of similar tasks based on the semantic similarities between learned objects. We also demonstrate, via experiments using Mybot, our ability to reach those goals that are not possible without the integration of semantic knowledge with episodic memory.

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Al-Moadhen, A., Qiu, R., Packianather, M., Ji, Z., & Setchi, R. (2013). Integrating robot task planner with common-sense knowledge base to improve the efficiency of planning. Procedia Computer Science, 22, 211–220.CrossRef

Benjamin, D. P., Lyons, D., & Lonsdale, D. (2004). Adapt: A cognitive architecture for robotics. In Proceedings of the international conference on cognitive modeling (pp. 337–338).

Carpenter, G., Grossberg, S., & Rosen, D. (1991). Fuzzy art: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks, 4, 759–771.CrossRef

Carpenter, G. A., & Grossberg, S. (1987). A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer Vision, Graphics, and Image Processing, 37, 54–115.CrossRef

Cunningham, P. (1998). CBR: Strengths and weaknesses. In Proceedings of the 11th international conference on industrial and engineering applications of artificial intelligence and expert systems: Tasks and methods in applied artificial intelligence (pp. 517–524).CrossRef

Dayoub, F., Duckett, T., & Cielniak, G. (2010). Toward an object-based semantic memory for long-term operation of mobile service robots. In IROS workshop on semantic mapping and autonomous knowledge acquisition.

Galindo, C., Fernandez-Madrigal, J., Gonzalez, J., & Saffiotti, A. (2008). Robot task planning using semantic maps. Robotics and Autonomous Systems, 56(11), 955–966.CrossRef

Gao, S., & Tan, A. H. (2014). A multi-memory modeling approach. In Proceedings of the international joint conference on neural networks (pp. 1542–1548).

Greenberg, D. L., & Verfaellie, M. (2015). Interdependence of episodic and semantic memory: Evidence from neuropsychology. Journal of the International Neuropsychological Society, 16, 748–753.CrossRef

Hawkins, J., George, D., & Niemasik, J. (2009). Sequence memory for prediction, inference and behaviour. Philosophical Transactions of the Royal Society B, 364, 1203–1209.CrossRef

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735–1780.CrossRef

Irish, M., & Piguet, O. (2013). The pivotal role of semantic memory in remembering the past and imagining the future. Frontiers in Behavioral Neuroscience. https://doi.org/10.3389/fnbeh.2013.00027.

Jeong, I. B., Lee, S. J., & Kim, J. H. (2015). RRT\(\ast \)-quick: A motion planning algorithm with faster convergence rate. In J. H. Kim, W. Yang, J. Jo, P. Sincak, & H. Myung (Eds.), Robot intelligence technology and applications 3. Advances in intelligent systems and computing (Vol. 345). Cham: Springer.

Ji, Z., Qiu, R., Noyvirt, A., Soroka, A., Packianather, M., Setchi, R., et al. (2012). Towards automated task planning for service robots using semantic knowledge representation. In Proceedings of the IEEE international conference on industrial information (pp. 1194–1201).

Kim, J. H., Choi, S. H., Park, I. W., & Zaheer, S. A. (2013). Intelligence technology for robots that think. IEEE Computational Intelligence Magazine, 8, 70–84.CrossRef

Laird, J. E., Newell, A., & Rosenbloom, P. S. (1987). Soar: An architecture for general intelligence. Artificial Intelligence, 33(1), 1–64. https://doi.org/10.1016/0004-3702(87)90050-6.CrossRef

Leconte, F., Ferland, F., & Michaud, F. (2015). Design and integration of a spatio-temporal memory with emotional influences to categorize and recall the experiences of an autonomous mobile robot. Autonomous Robots, 40, 831–848.CrossRef

Levine, B., Turner, G. R., Tisserand, D., Hevanor, S. J., Graham, S. J., & McIntosh, A. R. (2004). The functional neuroanatomy of episodic and semantic autobiographical remembering: A prospective functional mri study. Journal of Cognitive Neuroscience, 16, 1633–1646.CrossRef

McRae, K., & Jones, M. N. (2013). The Oxford handbook of cognitive psychology. Oxford: Oxford University Press (Chap Semantic memory).

Mermillod, M., Bugaiska, A., & Bonin, P. (2013). The stability–plasticity dilemma: Investigating the continuum from catastrophic forgetting to age-limited learning effects. Frontiers in Psychology, 4, 504. https://doi.org/10.3389/fpsyg.2013.00504.CrossRef

Nasir, J., & Kim, J. H. (2016). User preference-based integrated multi-memory neural model for improving the cognitive abilities of autonomous robots. Master’s thesis, Korea Advanced Institute of Science and Technology.

Nasir, J., Yoo, Y. H., Kim, D. H., & Kim, J. H. (2018). User preference-based dual-memory neural model with memory consolidation approach. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2294–2308. https://doi.org/10.1109/TNNLS.2017.2691260.CrossRef

Nuxoll, A. M., & Laird, J. E. (2007). Extending cognitive architecture with episodic memory. In Proceedings of the 22nd national conference on artificial intelligence AAAI’07(Vol. 2, pp. 1560–1565). New York: AAAI Press.

Nuxoll, A. M., & Laird, J. E. (2012). Enhancing intelligent agents with episodic memory. Cognitive Systems Research, 17–18, 34–48. https://doi.org/10.1016/j.cogsys.2011.10.002.CrossRef

Riesbeck, C. K., & Schank, R. (1989). Inside case-based reasoning. Hillsdale: L. Erlbaum Associates Inc.

Rogers III, J. G., & Christensen, H. I. (2013). Robot planning with a semantic map. In IEEE international conference on robotics and automation (pp. 2239–2244).

Shapiro, S., & Bona, J. P. (2009). The glair cognitive architecture. International Journal of Machine Consciousness, 2(2), 307–332. https://doi.org/10.1142/S1793843010000515.CrossRef

Stachowicz, D., & Kruijff, G. M. (2012). Episodic-like memory for cognitive robots. IEEE Transactions on Autonomous Mental Development, 4(1), 1–16. https://doi.org/10.1109/TAMD.2011.2159004.CrossRef

Starzyk, J. A., & He, H. (2007). Anticipation-based temporal sequences learning in hierarchical structure. IEEE Transactions on Neural Networks, 18(2), 344–358. https://doi.org/10.1109/TNN.2006.884681.CrossRef

Starzyk, J. A., & He, H. (2009). Spatio-temporal memories for machine learning: A long-term memory organization. IEEE Transactions on Neural Networks, 20, 768–780. https://doi.org/10.1109/TNN.2009.2012854.CrossRef

Subagdja, B., & Tan, A. H. (2012). iFALCON: A neural architecture for hierarchical planning. Neurocomputing, 86, 124–139. https://doi.org/10.1016/j.neucom.2012.01.008.CrossRef

Sucan, I. A., & Chitta, S. (2011). Moveit! http://moveit.ros.org.

Tan, A. H., Carpenter, G. A., & Grossberg, S. (2007). Intelligence through interaction: Towards a unified theory for learning. In D. Liu, S. Fei, Z. G. Hou, H. Zhang, & C. Sun (Eds.), Advances in neural networks—ISNN 2007 (pp. 1094–1103). Berlin: Springer.CrossRef

Tan, A. H., Feng, Y. H., & Ong, Y. S. (2010). A self-organizing neural architecture integrating desire, intention and reinforcement learning. Neurocomputing, 73(7–9), 1465–1477. https://doi.org/10.1016/j.neucom.2009.11.012.CrossRef

Taylor, S. E., Vineyard, C. M., Healy, M. J., Caudell, T. P., Cohen, N. J., Watson, P., et al. (2009). Memory in silico: Building a neuromimetic episodic cognitive model. In Proceedings of world congress on computer science and information engineering (Vol. 5, pp. 733–737).

Tscherepanow, M. (2010) Topoart: A topology learning hierarchical art network. In Proceedings of the international conference on artificial neural networks (pp. 157–167).

Tscherepanow, M., Kuhnel, S., & Riechers, S. (2012). Episodic clustering of data streams using a topology-learning neural network. In Proceedings of the European conference on artificial intelligence—Workshop on active and incremental learning (pp. 22–24).

Tulving, E. (1972). Episodic and semantic memory. New York: Academic.

Tulving, E. (1983). Elements of episodic memory. New York: Oxford University Press.

Tulving, E. (2002). Episodic memory: From mind to brain. Annual Review of Psychology, 53, 1–25.CrossRef

Veiga, T. A., Miraldo, P., Ventura, R., & Lima, P. U. (2016). Efficient object search for mobile robots in dynamic environments: Semantic map as an input for the decision maker. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (pp. 2745–2750).

Wang, D., & Arbib, M. A. (1990). Complex temporal sequence learning based on short-term memory. Proceedings of the IEEE, 78(9), 1536–1543. https://doi.org/10.1109/5.58329.CrossRef

Wang, D., & Yuwono, B. (1995). Anticipation-based temporal pattern generation. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 25, 615–628.CrossRef

Wang, L. (1999). Multi-associative neural networks and their application to learning and retrieving complex spatio-temporal sequences. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29, 73–82.CrossRef

Wang, W., Subagdja, B., Tan, A. H., & Starzyk, J. A. (2012a). Neural modeling of episodic memory: Encoding, retrieval, and forgetting. IEEE Transactions on Neural Networks and Learning Systems, 23(10), 1574–1586. https://doi.org/10.1109/TNNLS.2012.2208477.CrossRef

Wang, W., Subagdja, B., Tan, A. H., & Tan, Y. (2012b). A self-organizing multi-memory system for autonomous agents. In Proceedings of the international joint conference on neural networks (pp. 252–258).

Wang, W., Tan, A. H., & Teow, L. (2017). Semantic memory modeling and memory interaction in learning agents. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(11), 2882–2895. https://doi.org/10.1109/TSMC.2016.2531683.CrossRef

Wu, C., Lenz, I., & Saxena, A. (2014). Hierarchical semantic labeling for task-relevant RGB-D perception. In Robotics: Science and systems. https://doi.org/10.15607/RSS.2014.X.006.

Title: ART neural network-based integration of episodic memory and semantic memory for task planning for robots
Authors: Jauwairia Nasir
Deok-Hwa Kim
Jong-Hwan Kim
Publication date: 07-06-2019
Publisher: Springer US
Published in: Autonomous Robots / Issue 8/2019
Print ISSN: 0929-5593
Electronic ISSN: 1573-7527
DOI: https://doi.org/10.1007/s10514-019-09868-x

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