Visual Navigation Training With Domain Randomization for Robot Generalization
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Solution Overview
Problem
Navigating robots face challenges in adapting to different environments due to overfitting when trained in a single environment, leading to poor generalization and increased likelihood of collisions in real-world scenarios.
Innovation Solution
The training system employs domain randomization and invariance regularization, generating varied virtual training environments by altering characteristics like texture, color, and lighting, and using convolutional neural networks to train models for visual navigation, enabling the robot to generalize better across diverse settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a navigating robot is trained in a single environment, then the training process is simple and fast, but the model overfits and generalization performance deteriorates
Solution Approach 1:
The patent applies parameter changes by systematically varying environmental parameters (lighting conditions, textures, colors, object positions, camera angles) to generate diverse training environments from a single base environment. This allows the model to learn invariant features across different parameter configurations, improving generalization without requiring multiple physically distinct environments.
Solution Approach 2:
The patent creates multiple virtual copies of the training environment with different randomized parameters instead of using multiple physical environments. These copied and modified virtual environments provide diverse training scenarios while maintaining the same underlying structure, reducing the complexity of setting up multiple real-world training spaces.
2Reliability
If the training model uses multiple base virtual environments, then generalization improves, but training time and computational resources increase
Solution Approach 1:
The patent segments the training process into two phases: first training on N base virtual environments to learn fundamental navigation skills, then applying domain randomization to generate varied environments for fine-tuning generalization. This segmented approach allows efficient use of computational resources by focusing intensive randomization on a smaller set of base environments rather than maintaining many diverse environments throughout training.
Solution Approach 2:
The patent performs preliminary training on base environments before applying domain randomization variations. This preliminary action establishes a solid foundation of navigation skills that can then be robustified through randomization, rather than attempting to learn all variations simultaneously, which would be computationally prohibitive.
3Adaptability or versatility
If domain randomization is applied to generate varied training environments, then the robot's adaptability to unseen environments improves, but the complexity of the training system increases
Solution Approach 1:
The patent implements a universal training framework where a single base environment can generate multiple varied training scenarios through parameter randomization. This multi-functional approach allows one environment to serve multiple training purposes, reducing the need for numerous specialized environments and simplifying the overall training system architecture.
Solution Approach 2:
The training system uses automated domain randomization algorithms that self-generate diverse environment variations without manual intervention. The system automatically adjusts parameters, generates randomized environments, and trains the model across all variations, eliminating the need for manual environment design and reducing operational complexity.
Data Source
AI summary
A training system for training a trained model for use by a navigating robot to perform visual navigation includes memory including N base virtual training environments, each of the N base virtual training environments including a field of view at a location within an indoor space, where N is an integer greater than 1. A randomization module is configured to generate N varied virtual training environments based on the N base virtual training environments, respectively, by varying at least one characteristic of the respective N base virtual training environments. A training module is configured to train the trained model for use by the navigating robot to perform visual navigation based on a training set including: the N base virtual training environments; and the N varied virtual training environments.