Continual Learning for Robots and Cloud: A Novel Data Sampling Approach

This paper's main contribution is a novel data sampling approach for continual learning in robot-cloud systems, designed to ensure the diversity and timeliness of training data. It's built on two key ideas: 1) Utilizing the robot's prior knowledge to optimize sampling strategies, preserving crucial past experiences when new tasks emerge; 2) Leveraging the cloud system to monitor the robot's behavior and automatically adjust sampling strategies when needed.

The technical approach consists of the following steps:

1. The robot records its observed states and feedback while performing tasks and uploads this data to the cloud system.

2. The cloud system analyzes the uploaded data to calculate the probability distribution for each state.

3. Based on this distribution, the cloud system selects the most optimal states and sends them back to the robot.

4. The robot utilizes these selected states to execute the next task, again uploading its observed states and feedback to the cloud system.

5. The cloud system updates the state probability distribution based on the latest data, enabling more informed state selection for future tasks.

This process enables continuous learning for the robot across tasks, preserving past experiences while the cloud system automatically adapts the data sampling strategy to maintain diversity and timeliness. The approach is applicable to various robot-cloud systems, enhancing learning efficiency and overall performance.