ART neural network-based integration of episodic memory and semantic memory for task planning for robots
- 299 Downloads
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.
KeywordsAdaptive resonance theory Task planning Cognition Semantic memory Episodic memory User preference
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea (MSIP) (No. NRF-2014R1A2A1A10051551) and the Technology Innovation Program, 10045252, funded by the Korea MOTIE. The authors would like to thank Yong-Ho Yoo for his guidance during experiments on Mybot. The authors would also like to thank Jennifer Olsen, a post-doc at Computer–Human Interaction in Learning and Instruction Lab., for her feedback on the draft.
Supplementary material 1 (mp4 33522 KB)
- 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).Google Scholar
- 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.Google Scholar
- Gao, S., & Tan, A. H. (2014). A multi-memory modeling approach. In Proceedings of the international joint conference on neural networks (pp. 1542–1548).Google Scholar
- 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.Google Scholar
- 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).Google Scholar
- McRae, K., & Jones, M. N. (2013). The Oxford handbook of cognitive psychology. Oxford: Oxford University Press (Chap Semantic memory).Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Riesbeck, C. K., & Schank, R. (1989). Inside case-based reasoning. Hillsdale: L. Erlbaum Associates Inc.Google Scholar
- 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).Google Scholar
- Sucan, I. A., & Chitta, S. (2011). Moveit! http://moveit.ros.org.
- 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).Google Scholar
- Tscherepanow, M. (2010) Topoart: A topology learning hierarchical art network. In Proceedings of the international conference on artificial neural networks (pp. 157–167).Google Scholar
- 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).Google Scholar
- Tulving, E. (1972). Episodic and semantic memory. New York: Academic.Google Scholar
- Tulving, E. (1983). Elements of episodic memory. New York: Oxford University Press.Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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.