Neural Environment Generation With Memory for Consistent Simulation
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Solution Overview
Problem
Existing simulators for training artificial agents are complex, time-consuming to develop, and not easily scalable, requiring skilled graphics experts to create diverse and accurate simulated environments.
Innovation Solution
A simulation approach using a generative adversarial network (GAN) with a dynamics engine, memory module, and rendering engine to learn and generate realistic, temporally-coherent environments by disentangling static and dynamic elements, allowing agents to navigate and return to consistent locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex procedural models and behavior trees are used to generate simulated environments, then the accuracy and diversity of simulation scenarios are improved, but the development time and expertise requirements increase significantly
Solution Approach 1:
The patent replaces traditional mechanical/procedural simulation systems with a neural network-based generative model. Instead of using complex procedural models and behavior trees to generate simulation environments, the system trains a neural network to learn the underlying patterns and dynamics of real-world environments, enabling automated generation of accurate and diverse simulation scenarios without manual programming.
Solution Approach 2:
The generative model becomes self-sufficient by learning environment dynamics directly from data. The neural network automatically captures complex interactions and behaviors through training, eliminating the need for expert programmers to manually create procedural models and behavior trees for each simulation scenario.
2Adaptability or versatility
If complex procedural models are used to generate diverse scenes, then the diversity of simulation scenarios is improved, but the scalability of the system deteriorates
Solution Approach 1:
The patent replaces complex procedural generation systems with a data-driven neural network approach. The generative model learns diverse environment patterns from training data and can generate varied simulation scenarios through random sampling of learned distributions, eliminating the need for maintaining complex procedural models for each scenario type.
Solution Approach 2:
The trained neural network serves as a universal generator that can produce multiple types of simulation environments and scenarios from a single model. Instead of requiring separate procedural models for different scene types, the generative model generalizes across diverse environments, improving scalability while maintaining scenario diversity.
3Reliability
If skilled graphics experts manually create simulation environments, then the quality and realism of environments are improved, but the ease of manufacture and accessibility deteriorates
Solution Approach 1:
The system enables automated environment generation through the trained generative model, which independently learns realistic environment characteristics from training data. This eliminates the need for skilled graphics experts to manually craft simulation scenes, making high-quality environment creation accessible to users without specialized expertise.
Solution Approach 2:
The patent replaces manual expert craftsmanship with an automated neural network system. The generative model captures the subtle details and realism that previously required expert knowledge, transferring the quality assurance function from human experts to an automated system that can be operated by general users.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to generate a simulated environment. In at least one embodiment, one or more neural networks are used to generate a simulated environment based, at least in part, on stored information associated with objects within the simulated environment.