Artificial intelligence, machine learning and deep learning in advanced robotics, a review

Analysis

Why This Paper Matters

This review paper arrives at a critical juncture where robotics is rapidly transitioning from pre-programmed automation to intelligent, adaptive systems. With over 1000 citations, it has become a foundational reference for researchers and engineers seeking to understand how AI, ML, and DL can be systematically applied across diverse robotic domains. The paper's broad scope—covering autonomous navigation, collaborative robots, manufacturing, aviation, and transportation—makes it uniquely valuable as a one-stop survey for practitioners looking to identify opportunities for AI integration in their own robotic systems.

The paper matters because it synthesizes a fragmented field. While individual papers often focus on narrow applications (e.g., deep learning for object detection in autonomous vehicles), this review connects those dots, showing how the same underlying ML techniques can be repurposed for predictive maintenance in factories or natural language processing in cobots. For Neura Market's audience of AI practitioners, this paper provides a strategic map of where the field stands and where the gaps are, enabling informed decisions about research investments and technology adoption.

Technical Contributions

The paper's primary contribution is its taxonomy of AI/ML/DL applications in robotics:

• Autonomous Navigation: Reviews SLAM, path planning, and obstacle avoidance using reinforcement learning and deep neural networks.
• Object Recognition and Manipulation: Covers convolutional neural networks (CNNs) for grasping and manipulation tasks.
• Natural Language Processing: Discusses how LLMs and transformer models enable human-robot interaction.
• Predictive Maintenance: Highlights ML models for anomaly detection and failure prediction in robotic systems.
• Collaborative Robots (Cobots): Explores adaptive control and safety systems that allow robots to work alongside humans.
• Transportation and Aviation: Extends the discussion to autonomous vehicles, drone swarms, and airline operations optimization.

Results

As a review paper, the results are qualitative rather than quantitative. The paper does not report new experimental metrics or benchmark comparisons. Instead, it aggregates findings from the literature, noting that AI/ML/DL techniques have led to:

• Improved navigation accuracy and robustness in dynamic environments.
• Enhanced object manipulation success rates through deep learning-based grasp planning.
• Increased efficiency in manufacturing assembly lines via predictive maintenance reducing downtime.
• Better customer satisfaction in taxi services through route optimization and demand prediction.

The paper does not provide specific numerical improvements (e.g., percentage reduction in error rates), which limits its utility for practitioners seeking concrete performance benchmarks.

Significance

The broader impact of this review is twofold. First, it serves as an educational resource for newcomers to the field, offering a structured overview of how AI, ML, and DL can be leveraged in robotics. Second, by explicitly identifying gaps in the literature—such as the need for more robust real-world validation and integration of multiple AI techniques—it sets a research agenda for the community. For Neura Market's audience, the paper underscores the importance of cross-domain transfer learning and the potential for unified AI frameworks that can handle perception, planning, and control in a single pipeline. As robotics continues to permeate industries from manufacturing to healthcare, this review provides a timely synthesis that can accelerate the adoption of intelligent robotic systems.