The 'Brain' of Humanoid Robots – How Does Their AI System Work?

Okay, no problem. Imagine you're interacting with a very smart "iron buddy." Let's talk about how its brain works.

The "Brain" of a Humanoid Robot – How Does Its AI System Work?

Hello! I'm glad to discuss this topic with you. Comparing a humanoid robot's AI system to a "brain" is very apt. Just like humans, robots also need a central system to perceive the world, think through problems, and take action.

You can imagine the workflow of this "brain" as three main steps: "Sensing" -> "Cognition" -> "Action".

Step One: Sensing – The "Five Senses" and "Nerves"

First, the robot needs to be able to "see" and "hear" what's happening around it. It relies on various sensors to collect information, just like our eyes, ears, and skin.

• Eyes (Vision System): Typically high-definition cameras, or even 3D cameras that can perceive depth. The AI processes these images to identify objects like tables and chairs, recognize your face, and even understand your gestures. This is powered by Computer Vision (CV) technology.
• Ears (Auditory System): Through microphone arrays, the robot can hear you speak. The AI processes the sound, filters out noise, and then uses Natural Language Processing (NLP) technology to understand what you mean by "Hello" or "Please get me a glass of water."
• Touch/Balance (Other Sensors): The robot is equipped with various sensors, such as torque sensors in its joints (to feel how much force it's applying), pressure sensors on its feet (for balance), and gyroscopes (to help it sense if it's tilting).

All the raw data collected by these sensors, like electrical currents, is continuously transmitted to the "brain" for processing.

Step Two: Cognition – The "Cerebral Cortex"

This is the most crucial and complex part. When a massive amount of information flows in, the "brain" needs to understand it and decide "what to do next?"

This cognitive process primarily relies on several key capabilities:

1. Knowledge Base: Like our memories and common sense. The robot's "brain" contains a vast database storing knowledge about the world. For example, "cups are for holding water," "doors can be pushed open," and "people cannot walk through walls."

2. Decision Making & Planning: This is the "brain's" "logical thinking" ability. When you command it to "go to the kitchen and pour a glass of water," it doesn't just do it in one go. The AI breaks down this complex task into a series of smaller steps:

   • Plan a path from its current location to the kitchen.
   • Identify the kettle and cup in the kitchen.
   • Calculate how to extend its arm and with how much force to grasp the kettle.
   • Then calculate how to tilt the kettle to pour water into the cup without spilling.
   • Finally, plan the path to bring the glass of water back to you. Each step requires precise calculation to ensure smooth and safe execution.

3. Learning Capability: This is key to making robots increasingly intelligent. There are two main approaches:

   • Supervised Learning: Like a teacher instructing a student. Engineers show the robot thousands of images of "apples" and tell it, "This is an apple." Over time, it learns to recognize apples.
   • Reinforcement Learning: Similar to "trial and error." For example, when a robot learns to walk, it might fall repeatedly at first. But the AI system has a "reward mechanism": each time it successfully takes a stable step, it gets a "reward score." To earn more points, the robot continuously adjusts its gait on its own, eventually learning to walk steadily, and even run and jump. Boston Dynamics' robot dogs are masters in this area.

Step Three: Action – The "Cerebellum" and "Muscles"

Once the "brain" has processed the information and made a decision, it needs to command the "body" to execute.

• Motion Control: The AI system translates high-level commands like "walk forward" or "raise arm" into specific instructions for the dozens or even hundreds of motors (equivalent to "muscles") throughout the body. For example, "bend left knee joint 30 degrees" or "right shoulder motor output 50% power."
• Balance and Coordination: This process requires extremely high coordination, much like our cerebellum. When the robot walks, the AI must continuously fine-tune the movement of each joint in real-time based on the ground conditions and its body posture to ensure it doesn't fall. This is also why humanoid robots appear much harder to walk than wheeled robots.

Let's put it all together with an example:

Suppose you say to the robot: "Please hand me the red apple on the table."

1. Sensing:

   • The microphone receives the sound, and NLP technology converts it into a text command.
   • The camera scans the room, and CV technology identifies the location of the "table," "red object," "apple," and "you."

2. Cognition:

   • The "brain" understands the intent of the command: pick up the red apple and hand it to you.
   • It confirms in its knowledge base that an "apple" is a graspable object.
   • It plans the optimal path to walk to the table.
   • It calculates how far its arm needs to extend, how wide its hand needs to open, and how much force to apply to pick up the apple without crushing it.
   • It then plans the path from the table to you.

3. Action:

   • The AI sends commands to the leg motors, and the robot walks steadily towards the table.
   • Upon arrival, it sends precise commands to the arm and hand motors to complete the grasping action.
   • It turns, walks towards you, and hands over the apple.

The entire process is completed in an instant, appearing seamless, but behind it lies the collaborative work of countless sensors, algorithms, and computational units.

In summary, the "brain" of a humanoid robot is a complex system integrating sensing, cognition, and control. By mimicking human learning and decision-making processes, it makes us feel that it is becoming increasingly "intelligent" and more like a true "companion."