Technical terms explained:
Humanoid robots: Imagine a robot that can walk, run, and even carry a school bag, resembling a human in shape and doing many of the things a human can do.
Causal Transformer Model: This is a very clever computer program that allows robots to learn how to decide what to do next based on what's happening around them. Just like when you're playing a game, you need to decide what your next move is based on what's happening in the game.
Autoregression: The concept is to give the bot the ability to say what will happen based on what it has seen or done before. For example, if you go to the park every day after school, your friends may go to the park today after school.
Model-free reinforcement learning: This is a method of teaching a robot to learn by not telling the robot all the rules beforehand, but instead letting it try different actions on its own to see which ones will make it succeed in completing the task. It's like when you learn to ride a bike, you start with a few falls, but slowly you learn how to keep your balance.
Parallel training: Imagine that if there were many people learning the same thing at the same time, the whole learning process would be much faster. Parallel training is about having many robots learn at the same time, so that they can learn new things faster.
* Environment: This is a virtual world created in a computer where the robot can practice walking or other movements, just like you would control a character in a computer game, but the purpose here is for the robot to learn.
Dynamic arm swing: Just as a person naturally swings his arm when walking, the researchers asked the robot to do the same, helping it maintain balance and move naturally.
Situational adaptation: This means that the robot is able to change the way it behaves depending on the environment and situation. For example, it can walk normally on a flat surface, but when it encounters a slope, it adjusts its pace to fit the slope.
Through these technologies, humanoid robots are able to walk stably in a variety of environments, adapt to different terrains, and remain stable even in the face of unexpected challenges. These advancements open up a wide range of possibilities for robots in the future, including helping people complete dangerous, complex, or monotonous tasks.