Generative AI-Assisted Robot Task Planning in Industrial Applications

  • Authors

    • Narendra Karmarkar Mathematician and Computer Scientist, Tata Institute of Fundamental Research, India. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2024PII9T6H

    Published 11-03-2024

  • Generative Artificial Intelligence, Robot Task Planning, Industrial Robotics, Large Language Models, Autonomous Manufacturing, Intelligent Automation, Reinforcement Learning, Industry 4.0, Human–Robot Collaboration, Digital Twin

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    [1]
    N. Karmarkar, “Generative AI-Assisted Robot Task Planning in Industrial Applications”, IJIARE, vol. 7, no. 2, pp. 01–17, Nov. 2024, doi: 10.67228/30715725/IJIARE-2024PII9T6H.

  • Abstract

    The rapid evolution of Industry 4.0 has accelerated the adoption of autonomous robotic systems in intelligent manufacturing. Traditional robot task planning methods are limited in dynamic production environments due to their dependence on rule-based programming and deterministic algorithms. In contrast, Generative Artificial Intelligence (GenAI), including Large Language Models (LLMs) and multimodal foundation models, enables robots to understand natural language, perform contextual reasoning, generate adaptive task plans, and respond intelligently to changing industrial conditions. This paper presents a Generative AI-based framework for robotic task planning that integrates multimodal perception, semantic reasoning, LLM-based planning, reinforcement learning, and execution monitoring. The framework enhances planning accuracy, adaptability, collaboration, and operational efficiency while reducing execution errors and planning complexity. It is applicable to smart assembly, automated inspection, warehouse logistics, predictive maintenance, collaborative robotics, and flexible manufacturing. The study concludes that Generative AI offers a promising foundation for scalable, adaptive, and human-centric industrial automation, with future research directed toward continual learning, explainable AI, federated robotic intelligence, edge AI, and trustworthy autonomous decision-making.

  • References

    [1] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature Reviews Methods Primers, vol. 1, no. 1, pp. 1–21, 2021.

    [2] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Hoboken, NJ, USA: Pearson, 2021.

    [3] A. Kalra, M. Dogar, and S. Srinivasa, "Robot task and motion planning: A survey," Annual Review of Control, Robotics, and Autonomous Systems, vol. 5, pp. 313–339, 2022.

    [4] J. Kober, J. A. Bagnell, and J. Peters, "Reinforcement learning in robotics: A survey," International Journal of Robotics Research, vol. 41, no. 8, pp. 761–805, 2022.

    [5] A. Dosovitskiy et al., "An image is worth 16×16 words: Transformers for image recognition at scale," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 12, pp. 9151–9165, 2022.

    [6] T. Brown et al., "Language models are few-shot learners," Communications of the ACM, vol. 65, no. 8, pp. 126–135, 2022.

    [7] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA, USA: MIT Press, 2023.

    [8] J. Devlin, M. Chang, K. Lee, and K. Toutanova, "Large-scale language representation learning for intelligent decision systems," IEEE Intelligent Systems, vol. 38, no. 2, pp. 44–56, 2023.

    [9] M. T. Ribeiro, S. Singh, and C. Guestrin, "Explainable artificial intelligence for trustworthy autonomous systems," IEEE Computer, vol. 56, no. 4, pp. 48–57, 2023.

    [10] S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, "Image segmentation using deep learning: A survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 6, pp. 639–660, 2023.

    [11] OpenAI, "GPT-4 Technical Report," arXiv:2303.08774, 2023.

    [12] H. Touvron et al., "LLaMA: Open and efficient foundation language models," arXiv:2302.13971, 2023.

    [13] F. Tao, H. Zhang, A. Liu, and A. Y. C. Nee, "Digital twin in smart manufacturing: Recent advances and future trends," IEEE Transactions on Industrial Informatics, vol. 20, no. 1, pp. 152–167, 2024.

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