FOCUS ON AI
■ Basic navigation for AGVs : Automated Guided Vehicles ( AGVs ) used sensors to navigate factory floors , automating material transport .
■ Machine learning for predictive maintenance : Sensors embedded in machinery could feed data ( vibration , temperature , sound ) to machinelearning algorithms , detecting patterns indicating impending equipment failure . This predictive maintenance allowed proactive repairs , minimising downtime .
Perception AI brought significant benefits but also had its limitations . Systems were often designed for very specific tasks and struggled to generalise . Training also required massive amounts of labelled data , which was expensive and time-consuming to acquire . Robots could recognize patterns , but they lacked a deeper understanding of context . They could identify a “ screw ” but didn ’ t understand its function . Unexpected variations , such as a change in lighting , could easily throw the system off track .
Perception AI was a crucial first step , providing foundational capabilities , but it lacked the flexibility , adaptability and true understanding needed to unlock automation ’ s full potential .
Generative AI ( where we are now )
■ Generating synthetic training data : Generative AI can create synthetic data that is just as effective ( or more so ) than real-world data for training , especially for perception tasks .
■ Optimising designs : Generative AI can explore a vast design space , generating numerous variations of a product or process layout and evaluating their performance .
■ Simulating complex scenarios : Generative AI allows the creation of realistic virtual environments for testing and training robots in various scenarios , including dangerous or expensive ones .
While still in its early stages , particularly in industrial settings , Generative AI is demonstrating its potential to revolutionise robotics , paving the way for more intelligent ,
Image : Shutterstock . com . adaptable and autonomous machines . This sets the stage for Agentic and Physical AI .
Agentic and Physical AI ( where we are going )
Building on Generative AI , we are now entering an era where the digital and physical worlds blur , giving rise to Agentic and Physical AI — the next frontier in intelligent robotics .
Agentic AI is the crucial step beyond Generative AI . AI systems evolve from passive content generators to active AI agents that can make decisions , plan and pursue goals . Imagine a robot that understands its environment and can formulate a plan to achieve an objective , adapt to changes and learn from experiences . This involves :
■ Reasoning and planning : Generative AI models will be further developed to enable robots to reason about
While Perception AI gave robots basic senses , Generative AI is poised to elevate them to a whole new level of intelligence . This technology marks a fundamental shift from simply recognizing patterns to understanding , creating and even reasoning . Unlike traditional AI , focused on analysis and prediction , Generative AI can create new content , analyse intricate patterns and make decisions based on a more nuanced understanding of context .
Concretely , Large Language Models ( LLMs ), such as OpenAI ’ s GPT models , have captured the public ’ s imagination , showcasing Generative AI ’ s power to understand and generate human-like text . LLMs are a crucial stepping stone , demonstrating AI ’ s potential to understand complex information and generate appropriate responses — essential for intelligent robots .
Importantly , Generative AI ’ s core principles are extending beyond language to other modalities critical for robotics . Researchers are developing models that can create and manipulate images , videos , 3D models and even robot trajectories .
We are already seeing early industrial applications of Generative AI , for example :
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