Policy Distillation Data for Robotics: Annotating Teacher-Student Pairs for Compact Deployable Models

The rapid development of robotic systems is largely due to the use of increasingly sophisticated machine learning models capable of performing complex tasks of perception, decision-making, and control. One promising approach to solving this problem is policy distillation, which involves transferring knowledge from a complex, high-performance teacher model to a more compact, efficient student model while maintaining the highest possible level of performance.

The effectiveness of policy distillation depends not only on the models' architecture and training procedure but also on the quality of the data that reflects the relationship between the teacher and student policies.

Theoretical foundations of policy distillation

Policy distillation is a method for transferring knowledge from a complex, high-performance model, called the teacher model, to a smaller, more computationally efficient student model. The main goal of this approach is to create a compact model that can reproduce the teacher's behavior while using less memory, computing resources,, and energy.

The policy determines how the robot chooses an action depending on the current state of the system or information received from the environment. The robot can use data from cameras, distance sensors, force sensors, positioning systems, and other sources to make decisions. Based on this data, the model determines the most appropriate action, such as the direction of movement, speed, manipulator position, or other control commands.

In the “teacher-student” scheme, the teacher model usually has a more complex architecture and requires significant computing resources. It can be pre-trained to perform a certain robotic task with high accuracy. The teacher model is then used as a source of knowledge to train a more compact student model. The student receives similar input and gradually learns to reproduce the teacher’s decisions and behavior.

Policy distillation differs from traditional knowledge distillation. In typical machine learning tasks, the model often works with separate, independent examples. In robotics, decisions are sequential, as each action affects the system's subsequent state and future decisions.

Features of the application of policy distillation in robotics

The application of policy distillation in robotics has several features related to the need for models to operate in real environments and under resource constraints. The main ones are:

• Limited computing resources. Robotic platforms often use embedded computers and mobile processors, which have lower performance compared to powerful server systems.
• Limited memory. Large models may require significant RAM and permanent storage, so using them directly at work may be difficult or impossible.
• The need for real-time operation. The robot must respond quickly to environmental changes. A compact model allows you to reduce data processing and decision-making time.
• Low power consumption. For autonomous robots, it is important to use battery power economically. Reducing the model's complexity can lower system power consumption.
• Working with different types of data. Robotic systems can simultaneously use images, videos, sensor data, position information, and other signals. The learning model must effectively process the required types of information.
• Sequential nature of decision-making. Each action of the robot affects its subsequent state. Therefore, the learning model must reproduce not only individual decisions of the teacher, but also the overall strategy of behavior.
• Possibility of unforeseen situations. In the real environment, the robot may encounter conditions that were not present in the training data. Therefore, it is important to ensure a sufficient variety of examples when forming the data set.
• Reliability requirements. Model errors in robotics can lead to incorrect task performance or undesirable system behavior. Because of this, quality control of data and learning results is of particular importance.

Formation of teacher-student pairs

Data annotation

During annotation, it is necessary to describe the current state of the robot and the environment, sensor data, actions of the teacher model, and the results of their execution. For example, for a mobile robot, the annotation may contain information about the presence of obstacles, the direction of movement, the selected action, and its result. For a robotic manipulator, the object's position, the type of capture, and the success of the operation may be indicated.

Annotating erroneous and undesirable situations is an important aspect. These may include collisions with obstacles, unsuccessful capture of an object, incorrect movement, direction, or delays in executing a command.

It is also necessary to take into account the task's context. Lighting conditions, surface type, the presence of obstacles, and other factors can affect the behavior of the robotic system. Adding contextual information makes the dataset more complete and helps the model perform in a variety of conditions.

For sequential robotic tasks, it is important to maintain the correct order of events and data synchronization. Information from cameras, sensors, and control systems must correspond to the specific moment when the action is executed. A synchronization failure can lead to incorrect input-action pairs and adversely affect model training.

FAQ

What is a policy distillation dataset?

A policy distillation dataset is a collection of data designed to transfer knowledge from a complex teacher model to a compact student model. It contains observations, actions, and additional annotations required for effective training.

What role does teacher-student annotation play?

Teacher-student annotation establishes a connection between the teacher model's decisions and the student model's expected behavior. This annotation helps the compact model reproduce the teacher’s policy more accurately.

What do state-action pair data contain?

State-action pair data connect information about the current state of a robotic system with the corresponding action. These pairs form the basis for teaching a model to make appropriate decisions in specific situations.

Why is confidence threshold labeling used?

Confidence-threshold labeling allows training examples to be selected or labeled based on the teacher model’s confidence level. This helps reduce the influence of uncertain or unreliable decisions during training.

What are distribution shift markers?

Distribution shift markers identify situations in which new data differ from the data used during training. They help detect unusual conditions and evaluate the model's reliability.

How does data annotation affect compact model training?

High-quality annotation provides compact model training with accurate and consistent examples. This enables the student model to learn the teacher model’s behavior more effectively.

Why is the quality of a policy distillation dataset important?

Incorrect or incomplete annotations can lead to ineffective knowledge transfer. A high-quality policy distillation dataset improves the accuracy, stability, and reliability of the student model.

How is teacher-student annotation used in robotics?

In robotics, teacher-student annotation can connect sensor data with actions selected by the teacher model. The student model uses these examples to learn how to control the robot in different situations.

Why should state-action pair data include diverse situations?

Diverse state-action pair data help the model operate under different environmental conditions. This also reduces the risk of errors when the robot encounters new or unusual situations.

How do confidence threshold labeling and distribution shift markers improve compact model training?

Confidence threshold labeling helps control the quality of training examples, while distribution shift markers identify deviations from typical operating conditions. Together, they support more reliable compact model training and improve deployment in real-world robotic systems.