Training Robot AI Models Faster Using Smart Simulations

This patent describes a cloud service that helps train artificial intelligence models for robots by running simulations, even suggesting improvements to the AI's learning rules before starting.

What it covers

This patent details a computer-implemented method where a 'simulation management service' receives code from a customer. This code defines a 'reinforcement function' for training an AI model for a system, like a robot (Claim 1). The service then evaluates this code and suggests ways to improve it, based on past experiences with similar code (Claim 1). After modifying the code, the service creates a 'simulation environment' and injects the improved code into a 'simulation application' for the robot (Claim 1). Finally, it performs the reinforcement learning within this simulated world. For example, the simulation might select a robot's 'state' (like its position) and 'actions' (like moving forward), then provide a 'reward value' based on how well the action performed, which helps the AI model learn and improve (Claim 2, 4).

What it doesn't cover

• —Does not cover training reinforcement learning models directly on physical robots without using a simulation environment.
• —Does not cover systems that train AI models without first evaluating and suggesting modifications to the customer's reinforcement function code.
• —Does not cover other types of machine learning, like supervised or unsupervised learning, that do not involve a reinforcement function and reward-based training.
• —Does not cover a simulation system where the user's code is not injected into a pre-existing simulation application.
• —Does not cover a system that doesn't use prior code or historical data to generate suggestions for modifying the reinforcement function.

The clever bit

The truly clever part is the 'simulation management service' automatically evaluating the customer's reinforcement function code and suggesting modifications based on prior data. This proactive optimization helps ensure the AI model learns more efficiently and effectively before the simulation even begins.

Why it matters

Training complex AI models for robots or autonomous systems is difficult and expensive in the real world. This patent matters because it provides a structured, cloud-based way to accelerate this training in a safe, virtual environment. By automatically suggesting improvements to the learning code, it helps developers create more effective AI models faster, reducing development costs and time for applications like warehouse automation or self-driving vehicles.

Real-world examples

• 1.Amazon Web Services (AWS) RoboMaker
• 2.Cloud-based robotics simulation platforms
• 3.Autonomous vehicle training simulators
• 4.Industrial automation robot training
• 5.Logistics and warehouse robot pathfinding optimization