AI-Assisted Dexterous Manipulation Using Multi-Finger Robotic Hands

  • Authors

    • Narendra Karmarkar Mathematician and Computer Scientist, Tata Institute of Fundamental Research, India. Author
    • P. K. Iyengar Scientific Computing Researcher, BARC, India. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2020PII3X8N

    Published 12-04-2020

  • Artificial Intelligence, Dexterous Manipulation, Multi-Finger Robotic Hands, Deep Learning, Reinforcement Learning, Computer Vision, Robotic Grasping, Tactile Sensing, Sensor Fusion, Intelligent Robotics, Adaptive Control, Human-Robot Collaboration

    Issue

    Vol. 3 No. 2 (2020)

    Section

    Articles

    How to Cite

    [1]
    N. Karmarkar and I. P. K, “AI-Assisted Dexterous Manipulation Using Multi-Finger Robotic Hands”, IJIARE, vol. 3, no. 2, pp. 01–16, Dec. 2020, doi: 10.67228/30715725/IJIARE-2020PII3X8N.

  • Abstract

    Artificial intelligence (AI) has significantly advanced robotic manipulation by enabling multi-finger robotic hands to perform complex object-handling tasks with greater precision, adaptability, and autonomy. Unlike conventional robotic grippers, dexterous robotic hands possess multiple degrees of freedom (DOFs), allowing them to manipulate objects of varying shapes, sizes, and textures. Recent developments in deep learning, reinforcement learning, computer vision, tactile sensing, and multimodal sensor fusion enable robots to perceive, learn, and adapt through interaction rather than relying on predefined rules. AI techniques such as CNNs, Vision Transformers (ViTs), reinforcement learning, and tactile feedback improve object recognition, grasp planning, force control, and manipulation reliability in dynamic environments. This research presents a comprehensive review of AI-assisted dexterous manipulation, integrating multimodal perception, intelligent grasp planning, reinforcement learning, adaptive force control, and closed-loop feedback into a unified manipulation framework. It also introduces mathematical models and evaluation metrics for grasp stability, manipulation accuracy, response time, trajectory efficiency, and task completion. The proposed framework enhances manipulation success, robustness, adaptability to unseen objects, and learning capability compared to traditional rule-based systems, supporting applications in industrial automation, healthcare, logistics, agriculture, disaster response, and service robotics.

  • References

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