The Role of Machine Learning in Improving Robotic Perception and Decision Making
DOI:
https://doi.org/10.33050/italic.v3i1.661Keywords:
Machine Learning, Robotic Perception, Decision-Making, Deep Learning, Reinforcement LearningAbstract
Machine learning, specifically through Convolutional Neural Networks (CNNs) and Reinforcement Learning (RL), significantly enhances robotic perception and decision-making capabilities. This research explores the integration of CNNs to improve object recognition accuracy and employs sensor fusion for interpreting complex environments by synthesizing multiple sensory inputs. Furthermore, RL is utilized to refine robots real-time decision-making processes, which reduces task completion times and increases decision accuracy. Despite the potential, these advanced methods require extensive datasets and considerable computational resources for effective real-time applications. The study aims to optimize these machine learning models for better efficiency and address the ethical considerations involved in autonomous systems. Results indicate that machine learning can substantially advance robotic functionality across various sectors, including autonomous vehicles and industrial automation, supporting sustainable industrial growth. This aligns with the United Nations Sustainable Development Goals, particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 8 (Decent Work and Economic Growth), by promoting technological innovation and enhancing industrial safety. The conclusion suggests that future research should focus on improving the scalability and ethical application of these technologies in robotics, ensuring broad, sustainable impact.
References
M. Soori, B. Arezoo, and R. Dastres, “Artificial intelligence, machine learning and deep learning in advanced robotics, a review,” Cognitive Robotics, vol. 3, pp. 54–70, 2023.
D. Manongga, U. Rahardja, I. Sembiring, Q. Aini, and A. Wahab, “Improving the air quality monitoring framework using artificial intelligence for environmentally conscious development,” HighTech and Innovation Journal, vol. 5, no. 3, pp. 794–813, 2024.
F. Semeraro, A. Griffiths, and A. Cangelosi, “Human–robot collaboration and machine learning: A systematic review of recent research,” Robotics and Computer-Integrated Manufacturing, vol. 79, p. 102432, 2023.
I. Sembiring, D. Manongga, U. Rahardja, and Q. Aini, “Understanding data-driven analytic decision making on air quality monitoring an empirical study,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 3, pp. 418–431, 2024.
D. Kim, S.-H. Kim, T. Kim, B. B. Kang, M. Lee, W. Park, S. Ku, D. Kim, J. Kwon, H. Lee et al., “Review of machine learning methods in soft robotics,” Plos one, vol. 16, no. 2, p. e0246102, 2021.
S. Purnama and C. S. Bangun, “Strategic management insights into housewives consumptive shopping behavior in the post covid-19 landscape,” APTISI Transactions on Management, vol. 8, no. 1, pp. 71–79, 2024.
X. Xiao, B. Liu, G. Warnell, and P. Stone, “Motion planning and control for mobile robot navigation using machine learning: a survey,” Autonomous Robots, vol. 46, no. 5, pp. 569–597, 2022.
M. Irawan and Z. A. Tyas, “Desain asset game android komodo isle berbasis 2 dimensi,” ADI Bisnis Digital Interdisiplin Jurnal, vol. 5, no. 1, pp. 58–66, 2024.
Q. Bai, S. Li, J. Yang, Q. Song, Z. Li, and X. Zhang, “Object detection recognition and robot grasping based on machine learning: A survey,” IEEE access, vol. 8, pp. 181 855–181 879, 2020.
U. Rusilowati, H. R. Ngemba, R. W. Anugrah, A. Fitriani, and E. D. Astuti, “Leveraging ai for superior efficiency in energy use and development of renewable resources such as solar energy, wind, and bioenergy,” International Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 114–120, 2024.
R. Liu, F. Nageotte, P. Zanne, M. de Mathelin, and B. Dresp-Langley, “Deep reinforcement learning for the control of robotic manipulation: a focussed mini-review,” Robotics, vol. 10, no. 1, p. 22, 2021.
I. Erliyani, K. Yuliana, H. Kusumah, and N. Aziz, “Metode pembelajaran dalam memberikan pendidikan agama islam pada usia dini industri 4.0,” Alfabet Jurnal Wawasan Agama Risalah Islamiah, Teknologi dan Sosial, vol. 1, no. 1, pp. 96–105, 2021.
J. Kirman, A. Johnston, D. A. Kuntz, M. Askerka, Y. Gao, P. Todorovi´c, D. Ma, G. G. Prive, and E. H. Sargent, “Machine-learning-accelerated perovskite crystallization,” Matter, vol. 2, no. 4, pp. 938–947, 2020.
K. Myers and C. R. Hinman, “The impact of cryptocurrency on the indonesian community’s economy,” Blockchain Frontier Technology, vol. 3, no. 1, pp. 74–79, 2023.
O. Kroemer, S. Niekum, and G. Konidaris, “A review of robot learning for manipulation: Challenges, representations, and algorithms,” Journal of machine learning research, vol. 22, no. 30, pp. 1–82, 2021.
M. Pereira, I. Guvlor et al., “Implementation of artificial intelligence framework to enhance human re- sources competency in indonesia,” International Journal of Cyber and IT Service Management, vol. 4, no. 1, pp. 64–70, 2024.
X. Du, L. L¨uer, T. Heumueller, J. Wagner, C. Berger, T. Osterrieder, J. Wortmann, S. Langner, U. Vongsaysy, M. Bertrand et al., “Elucidating the full potential of opv materials utilizing a high throughput robot-based platform and machine learning,” Joule, vol. 5, no. 2, pp. 495–506, 2021.
V. Melinda, T. Williams, J. Anderson, J. G. Davies, and C. Davis, “Enhancing waste-to-energy conversion efficiency and sustainability through advanced artificial intelligence integration,” International Transactions on Education Technology (ITEE), vol. 2, no. 2, pp. 183–192, 2024.
N. L. Rane, S. P. Choudhary, and J. Rane, “Artificial intelligence-driven corporate finance: enhancing efficiency and decision-making through machine learning, natural language processing, and robotic process automation in corporate governance and sustainability,” Studies in Economics and Business Relations, vol. 5, no. 2, pp. 1–22, 2024.
Z. Kedah, “Use of e-commerce in the world of business,” Startupreneur Business Digital (SABDA Journal), vol. 2, no. 1, pp. 51 60, 2023.
X. Chen, “Ai in healthcare: Revolutionizing diagnosis and treatment through machine learning,” MZ Journal of Artificial Intelligence, vol. 1, no. 2, 2024.
A. G. Prawiyogi, A. S. Anwar et al., “Perkembangan internet of things (iot) pada sektor energi: Sistematik literatur review,” Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi, vol. 1, no. 2, pp. 187–197, 2023.
B. Shih, D. Shah, J. Li, T. G. Thuruthel, Y.-L. Park, F. Iida, Z. Bao, R. Kramer-Bottiglio, and M. T. Tolley, “Electronic skins and machine learning for intelligent soft robots,” Science Robotics, vol. 5, no. 41, p. eaaz9239, 2020.
A. Leffia, S. A. Anjani, M. Hardini, S. V. Sihotang, and Q. Aini, “Corporate strategies to improve platform economic performance: The role of technology, ethics, and investment management,” CORISINTA, vol. 1, no. 1, pp. 16–25, 2024.
B. Singh, R. Kumar, and V. P. Singh, “Reinforcement learning in robotic applications: a comprehensive survey,” Artificial Intelligence Review, vol. 55, no. 2, pp. 945–990, 2022.
M. R. Anwar and L. D. Sakti, “Integrating artificial intelligence and environmental science for sustainable urban planning,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 5, no. 2, pp. 179–191, 2024.
R. Ma, E. B. Vanstrum, R. Lee, J. Chen, and A. J. Hung, “Machine learning in the optimization of robotics in the operative field,” Current opinion in urology, vol. 30, no. 6, pp. 808–816, 2020.
F. S. Putri, H. R. Ngemba, S. Hendra, and W. Wirdayanti, “Sistem layanan ujian psikotes sim menggunakan computer based test berbasis website: Sim psychological test service system using computer based test based on website,” Technomedia Journal, vol. 9, no. 1, pp. 92–104, 2024.
S. H. Alsamhi, O. Ma, and M. S. Ansari, “Convergence of machine learning and robotics communication in collaborative assembly: mobility, connectivity and future perspectives,” Journal of Intelligent & Robotic Systems, vol. 98, no. 3, pp. 541–566, 2020.
H. Y. N. Heri, “The effect of fragmentation as a moderation on the relationship between supply chain management and project performance,” ADI Journal on Recent Innovation, vol. 6, no. 1, pp. 54–64, 2024.
D. Kalashnikov, J. Varley, Y. Chebotar, B. Swanson, R. Jonschkowski, C. Finn, S. Levine, and K. Hausman, “Scaling up multi-task robotic reinforcement learning,” in Conference on Robot Learning. PMLR, 2022, pp. 557–575.
R. Shimizu, S. Kobayashi, Y. Watanabe, Y. Ando, and T. Hitosugi, “Autonomous materials synthesis by machine learning and robotics,” APL Materials, vol. 8, no. 11, 2020.
J. R. Machireddy, “Leveraging robotic process automation (rpa) with ai and machine learning for scalable data science workflows in cloud-based data warehousing environments,” Australian Journal of Machine Learning Research & Applications, vol. 2, no. 2, pp. 234–261, 2022.
A. Alam, “Should robots replace teachers? mobilisation of ai and learning analytics in education,” in 2021 International Conference on Advances in Computing, Communication, and Control (ICAC3). IEEE, 2021, pp. 1–12.
L. Brunke, M. Greeff, A. W. Hall, Z. Yuan, S. Zhou, J. Panerati, and A. P. Schoellig, “Safe learning in robotics: From learning based control to safe reinforcement learning,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 5, no. 1, pp. 411 444, 2022.
H. Surmann, C. Jestel, R. Marchel, F. Musberg, H. Elhadj, and M. Ardani, “Deep reinforcement learning for real autonomous mobile robot navigation in indoor environments,” arXiv preprint arXiv:2005.13857, 2020.
J. Ibarz, J. Tan, C. Finn, M. Kalakrishnan, P. Pastor, and S. Levine, “How to train your robot with deep reinforcement learning: lessons we have learned,” The International Journal of Robotics Research, vol. 40, no. 4-5, pp. 698–721, 2021.
M. Paramesha, N. L. Rane, and J. Rane, “Artificial intelligence, machine learning, deep learning, and blockchain in financial and banking services: A comprehensive review,” Partners Universal Multidisciplinary Research Journal, vol. 1, no. 2, pp. 51–67, 2024.
Z. Liu, Q. Liu, W. Xu, L. Wang, and Z. Zhou, “Robot learning towards smart robotic manufacturing: A review,” Robotics and Computer-Integrated Manufacturing, vol. 77, p. 102360, 2022.
G. K. Sodhi, S. Kaur, G. S. Gaba, L. Kansal, A. Sharma, and G. Dhiman, “Covid-19: Role of robotics, artificial intelligence and machine learning during the pandemic,” Current medical imaging reviews, vol. 18, no. 2, pp. 124–134, 2022.
N. F. A. M. Fadzil, H. M. Fadzir, H. Mansor, and U. Rahardja, “Driver behaviour classification: A research using obd-ii data and machine learning,” Journal of Advanced Research in Applied Sciences and Engineering Technology, pp. 51–61, 2024.
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