Robotics & Physical AI

Robotics has been transformed by AI's ability to learn from data rather than relying on explicit programming for every possible scenario. Modern robots can learn manipulation tasks through trial and error in simulated environments, then transfer those skills to the real world. They can use computer vision to perceive and navigate unstructured environments, handle objects they have never seen before, and adapt to changing conditions. Warehouse robotics has seen the most commercial traction - picking, packing, and sorting systems powered by AI are now standard in large fulfilment centres. Surgical robotics, agricultural robots, and service robots for hospitality and healthcare are progressing steadily. The fundamental challenge remains dexterity and generalisation. Robots that can reliably perform one specific task in a controlled environment still struggle with the variety and unpredictability of real-world settings. A human can walk into an unfamiliar kitchen and make a cup of tea; a robot would need significant engineering to achieve the same thing. The gap between impressive demonstrations and reliable, cost-effective commercial deployment is still significant, but it is closing faster than many expected.