Of course. Here is an article on the topic.
The Brains Behind the Brawn: How AI is Giving Robots ‘Common Sense’
For decades, robots have been masters of the specific. Give one a car door to weld or a package to move from Point A to Point B, and it will perform the task with tireless precision. This is the “brawn”—the raw physical capability that has revolutionized manufacturing. But ask that same robot to tidy up a messy living room, and you’ll likely find it stacking a priceless vase on top of a half-eaten pizza.
The missing ingredient has always been the “brain,” specifically, the elusive, almost magical quality we call common sense.
Common sense is the unspoken symphony of rules that governs our world. It’s knowing that a glass is fragile but a brick is not; that you shouldn’t pour water on a laptop; that you hand a mug by its handle. For humans, this knowledge is second nature, learned through a lifetime of observation and interaction. For a robot, which operates on the brittle logic of pre-programmed code, it’s an almost insurmountable barrier.
Until now. A new wave of Artificial Intelligence is finally bridging this gap, transforming robots from mindless automatons into machines that can perceive, reason, and act with a semblance of genuine understanding.
The Problem: A World of Infinite “Ifs”
Traditional robotics is built on “if-then” statements. If an object is detected at coordinate X, then move gripper to X and close. This works beautifully in a controlled environment like a factory assembly line, where every variable is known.
The real world, however, is an unpredictable mess of infinite “ifs.” A robot tasked with cleaning a kitchen needs to understand not just what an object is, but its properties, its context, and the appropriate way to interact with it. Is that red liquid on the floor wine or ketchup? Should this knife be put in the dishwasher blade-up or blade-down? Is this crumpled paper trash or an important receipt?
Programming a robot for every single possibility is impossible. To become truly useful in our dynamic world, robots don’t need more code; they need to learn.
The AI Toolkit for a Smarter Robot
This is where modern AI comes in, providing a suite of tools that function as a robot’s developing brain.
1. Large Language Models (LLMs): The Reasoning Engine
The same technology behind chatbots like ChatGPT is becoming a robot’s internal monologue. LLMs are trained on vast swathes of the internet—books, articles, forums, and code. In doing so, they’ve absorbed an incredible amount of human knowledge about cause and effect, object properties, and social context.
A robot can now essentially “ask” an LLM for advice.
- Robot’s Vision System: “I see a transparent cylinder containing a brown liquid.”
- Robot’s Query to LLM: “How should I handle a glass of coffee?”
- LLM’s Response: “Grasp it firmly but gently around the middle. Keep it upright to avoid spilling. It is likely hot. Place it on a coaster or in the sink.”
This allows the robot to move from simple object recognition to complex, multi-step planning based on a deep well of contextual knowledge. Google’s robotics division, for example, has demonstrated robots that can interpret a vague command like “I’m hungry, bring me a snack” by using an LLM to reason that a bag of chips is a good option and an apple is a healthier one, then locating it and bringing it over.
2. Computer Vision: Understanding, Not Just Seeing
Modern AI has supercharged a robot’s ability to see. It’s no longer just about identifying a cat in a photo. Advanced computer vision models can perform “scene segmentation,” breaking down an entire room into individual objects and understanding their relationships.
It can see a “cup” on the “edge” of a “table” and infer that it’s in a precarious position. It can differentiate between a “clean plate” and a “dirty plate,” not just by looking for leftover food, but by understanding the context of where it is—in the cupboard versus next to the sink.
3. Simulation and Reinforcement Learning: The Digital Sandbox
You can’t teach a robot how to handle a delicate egg by letting it break a thousand of them. But you can in a virtual world.
Through a process called reinforcement learning in simulation, AI models can control a virtual robot in a “digital twin” of a real environment. Here, the robot can practice a task—like picking up a wine glass or opening a drawer—millions of times. It fails over and over, but with each failure, the AI learns what not to do. It’s rewarded for successful actions, slowly building an intuitive “feel” for physics and interaction. This learned intuition is then transferred to the physical robot.
This is how robots are learning dexterity—the brawn is being refined by a brain that has already made all its mistakes in the digital realm.
The Dawn of Truly Helpful Machines
When these technologies converge, something remarkable happens. A robot equipped with this AI toolkit doesn’t just follow instructions; it interprets intent.
A command to “clean up the spill” sets off a chain of reasoning:
- Vision: Identify the liquid and the surface (e.g., milk on a hardwood floor).
- LLM Query: “What’s the best way to clean milk from wood?”
- Reasoning: The LLM suggests using a paper towel first, then a damp cloth, and finally a dry one to prevent damage to the wood.
- Planning: The robot formulates a plan: fetch paper towels from the pantry, go to the spill, wipe, dispose of the towel, fetch a cloth, wet it at the sink, and so on.
- Execution (with learned dexterity): It carries out the plan, applying the right amount of pressure and using the fine motor skills it honed in simulation.
While we are still in the early days, the path is clear. The future of robotics isn’t just about stronger, faster machines. It’s about imbuing them with the common sense that will allow them to become true partners in our lives—in our homes, hospitals, and workplaces. The brawn has always been there; now, finally, the brain is catching up.
