Research Article
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Design of the Integrated Cognitive Perception Model for Developing Situation-Awareness of an Autonomous Smart Agent

Year 2023, Volume: 11 Issue: 3, 283 - 292, 21.08.2023
https://doi.org/10.17694/bajece.1310607

Abstract

This study explores the potential for autonomous agents to develop environmental awareness through perceptual attention. The main objective is to design a perception system architecture that mimics human-like perception, enabling smart agents to establish effective communication with humans and their surroundings. Overcoming the challenges of modeling the agent's environment and addressing the coordination issues of multi-modal perceptual stimuli is crucial for achieving this goal. Existing research falls short in meeting these requirements, prompting the introduction of a novel solution: a cognitive multi-modal integrated perception system. This computational framework incorporates fundamental feature extraction, recognition tasks, and spatial-temporal inference while facilitating the modeling of perceptual attention and awareness. To evaluate its performance, experimental tests and verification are conducted using a software framework integrated into a sandbox game platform. The model's effectiveness is assessed through a simple interaction scenario. The study's results demonstrate the successful validation of the proposed research questions.

Supporting Institution

ITU - Artificial Intelligence and Data Science Research Center / Cognitive Systems Lab

Thanks

I would like to express my special thanks to Cognitive Systems Laboratory (CSL) as well as Artificial Intelligence and Data Science Research Center (ITUAI), Istanbul Technical University for their encouragement and opportunity throughout this research.

References

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  • [5] Thiebaut de Schotten, M., & Forkel, S. J. (2022). The emergent properties of the connected brain. Science, 378(6619), 505-510.
  • [6] Li, B., Solanas, M. P., Marrazzo, G., Raman, R., Taubert, N., Giese, M., ... & de Gelder, B. (2023). A large-scale brain network of species-specific dynamic human body perception. Progress in Neurobiology, 221, 102398.
  • [7] Devia, C., Concha-Miranda, M., & Rodríguez, E. (2022). Bi-Stable Perception: Self-Coordinating Brain Regions to Make-Up the Mind. Frontiers in Neuroscience, 15, 805690.
  • [8] Taylor, A., Chan, D. M., & Riek, L. D. (2020). Robot-centric perception of human groups. ACM Transactions on Human-Robot Interaction (THRI), 9(3), 1-21.
  • [9] Ronchi, M. R. (2020). Vision for Social Robots: Human Perception and Pose Estimation (Doctoral dissertation, California Institute of Technology).
  • [10] Suzuki, R., Karim, A., Xia, T., Hedayati, H., & Marquardt, N. (2022, April). Augmented reality and robotics: A survey and taxonomy for ar-enhanced human-robot interaction and robotic interfaces. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-33).
  • [11] Farouk, M. (2022). Studying Human Robot Interaction and Its Characteristics. International Journal of Computations, Information and Manufacturing (IJCIM), 2(1).
  • [12] Müller, S., Wengefeld, T., Trinh, T. Q., Aganian, D., Eisenbach, M., & Gross, H. M. (2020). A Multi-Modal Person Perception Framework for Socially Interactive Mobile Service Robots. Sensors, 20(3), 722.
  • [13] Russo, C., Madani, K., & Rinaldi, A. M. (2020). Knowledge Acquisition and Design Using Semantics and Perception: A Case Study for Autonomous Robots. Neural Processing Letters, 1-16.
  • [14] Cangelosi, A., & Asada, M. (Eds.). (2022). Cognitive robotics. MIT Press.
  • [15] Iosifidis, A., & Tefas, A. (Eds.). (2022). Deep Learning for Robot Perception and Cognition. Academic Press.
  • [16] Lee, C. Y., Lee, H., Hwang, I., & Zhang, B. T. (2020, June). Visual Perception Framework for an Intelligent Mobile Robot. In 2020 17th International Conference on Ubiquitous Robots (UR) (pp. 612-616). IEEE.
  • [17] Mazzola, C., Aroyo, A. M., Rea, F., & Sciutti, A. (2020, March). Interacting with a Social Robot Affects Visual Perception of Space. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (pp. 549-557).
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  • [19] Sanneman, L., & Shah, J. A. (2020, May). A Situation Awareness-Based Framework for Design and Evaluation of Explainable AI. In International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems (pp. 94-110). Springer, Cham.
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  • [23] Sakai, T., & Nagai, T. (2022). Explainable autonomous robots: A survey and perspective. Advanced Robotics, 36(5-6), 219-238.
  • [24] Inceoglu, A., Koc, C., Kanat, B. O., Ersen, M., & Sariel, S. (2018). Continuous visual world modeling for autonomous robot manipulation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(1), 192-205.
  • [25] Kim, K., Sano, M., De Freitas, J., Haber, N., & Yamins, D. (2020). Active World Model Learning in Agent-rich Environments with Progress Curiosity. In Proceedings of the International Conference on Machine Learning (Vol. 8).
  • [26] Kim, K., Sano, M., De Freitas, J., Haber, N., & Yamins, D. (2020). Active World Model Learning with Progress Curiosity. arXiv preprint arXiv:2007.07853.
  • [27] Riedelbauch, D., & Henrich, D. (2019, May). Exploiting a Human-Aware World Model for Dynamic Task Allocation in Flexible Human-Robot Teams. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 6511-6517). IEEE.
  • [28] Rosinol, A., Gupta, A., Abate, M., Shi, J., & Carlone, L. (2020). 3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans. arXiv preprint arXiv:2002.06289.
  • [29] Venkataraman, A., Griffin, B., & Corso, J. J. (2019). Kinematically-Informed Interactive Perception: Robot-Generated 3D Models for Classification. arXiv preprint arXiv:1901.05580.
  • [30] Persson, A., Dos Martires, P. Z., De Raedt, L., & Loutfi, A. (2019). Semantic relational object tracking. IEEE Transactions on Cognitive and Developmental Systems, 12(1), 84-97.
  • [31] Zuidberg Dos Martires, P., Kumar, N., Persson, A., Loutfi, A., & De Raedt, L. (2020). Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring. arXiv, arXiv-2002.
  • [32] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.
  • [33] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
  • [34] LeCun, Y., & Bengio, Y. (1995). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10), 1995.
  • [35] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
  • [36] Sainath, T. N., Vinyals, O., Senior, A., & Sak, H. (2015, April). Convolutional, long short-term memory, fully connected deep neural networks. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4580-4584). IEEE.
  • [37] Chiu, H. P., Samarasekera, S., Kumar, R., Matei, B. C., & Ramamurthy, B. (2020). U.S. Patent Application No. 16/523,313.
  • [38] Wang, S., Wu, T., & Vorobeychik, Y. (2020). Towards Robust Sensor Fusion in Visual Perception. arXiv preprint arXiv:2006.13192.
  • [39] Xue, T., Wang, W., Ma, J., Liu, W., Pan, Z., & Han, M. (2020). Progress and prospects of multi-modal fusion methods in physical human-robot interaction: A Review. IEEE Sensors Journal.
  • [40] Guss, W. H., Codel, C., Hofmann, K., Houghton, B., Kuno, N., Milani, S., ... & Wang, P. (2019). Neurips 2019 competition: The minerl competition on sample efficient reinforcement learning using human priors. arXiv preprint arXiv:1904.10079.
  • [41] MineRL: A Large-Scale Dataset of Minecraft Demonstrations
  • [42] Frazier, S., & Riedl, M. (2019, October). Improving deep reinforcement learning in Minecraft with action advice. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 15, No. 1, pp. 146-152).
  • [43] Aluru, K. C., Tellex, S., Oberlin, J., & MacGlashan, J. (2015, September). Minecraft as an experimental world for AI in robotics. In the 2015 AAAI fall symposium series.
  • [44] Angulo, E., Lahuerta, X., & Roca, O. (2020). Reinforcement Learning in Minecraft.
  • [45] Eraldemir, S. G., Arslan, M. T., & Yildirim, E. (2018). Investigation of feature selection algorithms on A cognitive task classification: a comparison study. Balkan Journal of Electrical and Computer Engineering, 6(2), 99-104.
  • [46] Akinci, T. Ç., & Martinez-Morales, A. A. (2022). Cognitive Based Electric Power Management System. Balkan Journal of Electrical and Computer Engineering, 10(1), 85-90.
Year 2023, Volume: 11 Issue: 3, 283 - 292, 21.08.2023
https://doi.org/10.17694/bajece.1310607

Abstract

References

  • [1] Yan, Z., Schreiberhuber, S., Halmetschlager, G., Duckett, T., Vincze, M., & Bellotto, N. (2020). Robot Perception of Static and Dynamic Objects with an Autonomous Floor Scrubber. arXiv preprint arXiv:2002.10158.
  • [2] Freud, E., Behrmann, M., & Snow, J. C. (2020). What Does Dorsal Cortex Contribute to Perception?. Open Mind, 1-18.
  • [3] Bear, M., Connors, B., & Paradiso, M. A. (2020). Neuroscience: Exploring the brain. Jones & Bartlett Learning, LLC.
  • [4] Chin, R., Chang, S. W., & Holmes, A. J. (2022). Beyond cortex: The evolution of the human brain. Psychological Review.
  • [5] Thiebaut de Schotten, M., & Forkel, S. J. (2022). The emergent properties of the connected brain. Science, 378(6619), 505-510.
  • [6] Li, B., Solanas, M. P., Marrazzo, G., Raman, R., Taubert, N., Giese, M., ... & de Gelder, B. (2023). A large-scale brain network of species-specific dynamic human body perception. Progress in Neurobiology, 221, 102398.
  • [7] Devia, C., Concha-Miranda, M., & Rodríguez, E. (2022). Bi-Stable Perception: Self-Coordinating Brain Regions to Make-Up the Mind. Frontiers in Neuroscience, 15, 805690.
  • [8] Taylor, A., Chan, D. M., & Riek, L. D. (2020). Robot-centric perception of human groups. ACM Transactions on Human-Robot Interaction (THRI), 9(3), 1-21.
  • [9] Ronchi, M. R. (2020). Vision for Social Robots: Human Perception and Pose Estimation (Doctoral dissertation, California Institute of Technology).
  • [10] Suzuki, R., Karim, A., Xia, T., Hedayati, H., & Marquardt, N. (2022, April). Augmented reality and robotics: A survey and taxonomy for ar-enhanced human-robot interaction and robotic interfaces. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-33).
  • [11] Farouk, M. (2022). Studying Human Robot Interaction and Its Characteristics. International Journal of Computations, Information and Manufacturing (IJCIM), 2(1).
  • [12] Müller, S., Wengefeld, T., Trinh, T. Q., Aganian, D., Eisenbach, M., & Gross, H. M. (2020). A Multi-Modal Person Perception Framework for Socially Interactive Mobile Service Robots. Sensors, 20(3), 722.
  • [13] Russo, C., Madani, K., & Rinaldi, A. M. (2020). Knowledge Acquisition and Design Using Semantics and Perception: A Case Study for Autonomous Robots. Neural Processing Letters, 1-16.
  • [14] Cangelosi, A., & Asada, M. (Eds.). (2022). Cognitive robotics. MIT Press.
  • [15] Iosifidis, A., & Tefas, A. (Eds.). (2022). Deep Learning for Robot Perception and Cognition. Academic Press.
  • [16] Lee, C. Y., Lee, H., Hwang, I., & Zhang, B. T. (2020, June). Visual Perception Framework for an Intelligent Mobile Robot. In 2020 17th International Conference on Ubiquitous Robots (UR) (pp. 612-616). IEEE.
  • [17] Mazzola, C., Aroyo, A. M., Rea, F., & Sciutti, A. (2020, March). Interacting with a Social Robot Affects Visual Perception of Space. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (pp. 549-557).
  • [18] Mariacarla, B. Special Issue on Behavior Adaptation, Interaction, and Artificial Perception for Assistive Robotics.
  • [19] Sanneman, L., & Shah, J. A. (2020, May). A Situation Awareness-Based Framework for Design and Evaluation of Explainable AI. In International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems (pp. 94-110). Springer, Cham.
  • [20] Kridalukmana, R., Lu, H. Y., & Naderpour, M. (2020). A supportive situation awareness model for human-autonomy teaming in collaborative driving. Theoretical Issues in Ergonomics Science, 1-26.
  • [21] Tropmann-Frick, M., & Clemen, T. (2020). Towards Enhancing of Situational Awareness for Cognitive Software Agents. In Modellierung (Companion) (pp. 178-184).
  • [22] Gu, R., Jensen, P. G., Poulsen, D. B., Seceleanu, C., Enoiu, E., & Lundqvist, K. (2022). Verifiable strategy synthesis for multiple autonomous agents: a scalable approach. International Journal on Software Tools for Technology Transfer, 24(3), 395-414.
  • [23] Sakai, T., & Nagai, T. (2022). Explainable autonomous robots: A survey and perspective. Advanced Robotics, 36(5-6), 219-238.
  • [24] Inceoglu, A., Koc, C., Kanat, B. O., Ersen, M., & Sariel, S. (2018). Continuous visual world modeling for autonomous robot manipulation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(1), 192-205.
  • [25] Kim, K., Sano, M., De Freitas, J., Haber, N., & Yamins, D. (2020). Active World Model Learning in Agent-rich Environments with Progress Curiosity. In Proceedings of the International Conference on Machine Learning (Vol. 8).
  • [26] Kim, K., Sano, M., De Freitas, J., Haber, N., & Yamins, D. (2020). Active World Model Learning with Progress Curiosity. arXiv preprint arXiv:2007.07853.
  • [27] Riedelbauch, D., & Henrich, D. (2019, May). Exploiting a Human-Aware World Model for Dynamic Task Allocation in Flexible Human-Robot Teams. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 6511-6517). IEEE.
  • [28] Rosinol, A., Gupta, A., Abate, M., Shi, J., & Carlone, L. (2020). 3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans. arXiv preprint arXiv:2002.06289.
  • [29] Venkataraman, A., Griffin, B., & Corso, J. J. (2019). Kinematically-Informed Interactive Perception: Robot-Generated 3D Models for Classification. arXiv preprint arXiv:1901.05580.
  • [30] Persson, A., Dos Martires, P. Z., De Raedt, L., & Loutfi, A. (2019). Semantic relational object tracking. IEEE Transactions on Cognitive and Developmental Systems, 12(1), 84-97.
  • [31] Zuidberg Dos Martires, P., Kumar, N., Persson, A., Loutfi, A., & De Raedt, L. (2020). Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring. arXiv, arXiv-2002.
  • [32] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.
  • [33] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
  • [34] LeCun, Y., & Bengio, Y. (1995). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10), 1995.
  • [35] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
  • [36] Sainath, T. N., Vinyals, O., Senior, A., & Sak, H. (2015, April). Convolutional, long short-term memory, fully connected deep neural networks. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4580-4584). IEEE.
  • [37] Chiu, H. P., Samarasekera, S., Kumar, R., Matei, B. C., & Ramamurthy, B. (2020). U.S. Patent Application No. 16/523,313.
  • [38] Wang, S., Wu, T., & Vorobeychik, Y. (2020). Towards Robust Sensor Fusion in Visual Perception. arXiv preprint arXiv:2006.13192.
  • [39] Xue, T., Wang, W., Ma, J., Liu, W., Pan, Z., & Han, M. (2020). Progress and prospects of multi-modal fusion methods in physical human-robot interaction: A Review. IEEE Sensors Journal.
  • [40] Guss, W. H., Codel, C., Hofmann, K., Houghton, B., Kuno, N., Milani, S., ... & Wang, P. (2019). Neurips 2019 competition: The minerl competition on sample efficient reinforcement learning using human priors. arXiv preprint arXiv:1904.10079.
  • [41] MineRL: A Large-Scale Dataset of Minecraft Demonstrations
  • [42] Frazier, S., & Riedl, M. (2019, October). Improving deep reinforcement learning in Minecraft with action advice. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 15, No. 1, pp. 146-152).
  • [43] Aluru, K. C., Tellex, S., Oberlin, J., & MacGlashan, J. (2015, September). Minecraft as an experimental world for AI in robotics. In the 2015 AAAI fall symposium series.
  • [44] Angulo, E., Lahuerta, X., & Roca, O. (2020). Reinforcement Learning in Minecraft.
  • [45] Eraldemir, S. G., Arslan, M. T., & Yildirim, E. (2018). Investigation of feature selection algorithms on A cognitive task classification: a comparison study. Balkan Journal of Electrical and Computer Engineering, 6(2), 99-104.
  • [46] Akinci, T. Ç., & Martinez-Morales, A. A. (2022). Cognitive Based Electric Power Management System. Balkan Journal of Electrical and Computer Engineering, 10(1), 85-90.
There are 46 citations in total.

Details

Primary Language English
Subjects Electrical Engineering (Other)
Journal Section Araştırma Articlessi
Authors

Evren Dağlarlı 0000-0002-8754-9527

Early Pub Date August 21, 2023
Publication Date August 21, 2023
Published in Issue Year 2023 Volume: 11 Issue: 3

Cite

APA Dağlarlı, E. (2023). Design of the Integrated Cognitive Perception Model for Developing Situation-Awareness of an Autonomous Smart Agent. Balkan Journal of Electrical and Computer Engineering, 11(3), 283-292. https://doi.org/10.17694/bajece.1310607

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