"Using LLM to Control Motors?" This is how we teach AI to balance

We first establish a virtual world → Then define the physical rules and interaction methods of this world → Let AI repeatedly try and learn in this world → Finally, verify whether it has truly learned how to control the operation of this world.

1

Establishing the Simulation Environment: Building the Stage for AI Learning

We designed a customized simulation environment from scratch, using the Gym framework as the foundation. This simulated world mimics the real physical scenario: the cart can move horizontally, and a pendulum is mounted on the cart, with the goal of keeping the pendulum upright. In this stage, we clearly defined:

• The length, mass, and moment of inertia of the pendulum

• The speed limit of the cart's movement

• Natural physical constants like gravitational acceleration

• The discrete time step (i.e., the time unit for each simulation step)

These parameters make the simulation system able to realistically reflect the laws of physical motion, becoming the "laboratory" for AI models to interact and learn.

2

Defining Control Logic and Scoring Rules: Letting AI Know What is a "Good Action"

Next, we designed the action control and reward logic, which is to tell the AI: "What kind of actions are considered successful". This part includes:

• How to update the cart's position based on the "thrust" provided by the AI

• How to calculate the angle and acceleration changes of the pendulum using physical formulas

• The closer the pendulum is to vertical (180 degrees), the higher the reward the AI gets; if it deviates too far, points are deducted

• When both the angle and angular velocity are stabilized, the task is considered successful and the episode can be terminated early

These rules are the basis for the AI to judge the "effect of each action", which is the so-called Reward Function, a core design element in reinforcement learning.

3

Training the AI Model: Learning to Control the World through Reinforcement Learning

With the environment and rules in place, we used one of the most common reinforcement learning algorithms today: PPO (Proximal Policy Optimization) to train the AI model.

The training process can be likened to a child learning to walk: the AI constantly tries, fails, and then corrects. The model updates its strategy based on the results of each attempt, so that it can make better decisions when faced with similar situations in the future.

The key parameters we set include:

• Learning rate: Affects the speed and magnitude of model updates.

• Batch size: Determines the number of samples taken in each training round, which relates to the stability of model learning.

• Discount factor (gamma): Reflects the AI's emphasis on future rewards, with values closer to 1 indicating a greater focus on long-term rewards.

Through tens of thousands of simulations and learning, the AI model ultimately stabilized the cart's movement and maintained the pendulum's upright position, achieving the goal of establishing a world model from experience and formulating the optimal control strategy.

4

Testing and Visualization: Verifying if the AI Has Really Learned

Finally, we repeatedly tested the trained model and presented the entire control process in the form of animation.

The dynamic images clearly show:

• How the cart quickly adjusts its position to the left and right to maintain balance

• The pendulum remains stable even in a dynamic environment

• Whether the changes in angle and velocity at each time point are as expected

What is Gym?
Gym is a reinforcement learning simulation environment package developed by OpenAI, which provides standardized "Observation Space", "Action Space", "Reward Function", and "Environment State Transition Logic", allowing researchers to quickly build virtual worlds for AI to interact and train. You can think of Gym as an "AI-exclusive virtual lab" where AI can learn to try, make mistakes, and correct, just like a child, until it masters the control logic.

What is PPO?
PPO is a policy-based reinforcement learning algorithm that can "stably and slightly adjust the behavior policy" during each update, avoiding over-learning that leads to unstable performance. Compared to traditional algorithms, PPO combines learning efficiency and stability, and is widely used in areas like robot control, game AI, and autonomous vehicle simulation. By "limiting the update range", it ensures that each learning step is towards a better direction, but not too far off, similar to a "safe learning method of step-by-step behavior correction".

1. The Ability to Dynamically Understand the World (World Modeling)
   Through repeated interactions with the simulation environment, LLMs can master causal logic such as "applying force will cause displacement" and "changes in angle will affect balance", and then predict the future state of the system. This is what is called the world model. AI not only remembers the results, but also understands the process, abstracting a stable and transferable internal model from physical reactions, which also lays a critical foundation for areas like robot control, smart manufacturing, and digital twins.
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1. The Ability to Translate from Language Reasoning to Physical Control (Language-to-Action)
   The real breakthrough of language models lies in their ability to derive executable control strategies from natural language understanding. We no longer need to hard-code every instruction condition, but only need to input a simple command: "Keep the cart's pendulum upright". The LLM can then generate corresponding action plans based on the learned world model, and execute specific actions through the training results of reinforcement learning. This "language-to-action" capability makes language a control interface for driving motors, devices, and equipment in the real world, enabling the translation between semantics and behavior, and ushering in a new era of more natural and intuitive human-machine interaction.
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1. The Generalization Capability to Adapt to Unknown Environments
   Past control systems were often limited to preset scenarios and could not effectively cope with new changes or unexpected situations. However, this experiment demonstrates that LLMs combined with reinforcement learning can still stably control the inverted pendulum under different initial states, showing their strong generalization capability. This means the model is not just "memorizing solutions", but truly learning how to respond optimally based on the current situation. In the future, regardless of different friction, pendulum lengths, or device sizes, these models will be able to quickly adjust their strategies, exhibiting cross-domain and cross-scenario adaptability, which is crucial for actual deployment in factory floors, home appliances, or diverse outdoor robot environments.
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