Domain-Invariant Image Preprocessing for Robot Vision
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Solution Overview
Problem
Training neural networks with synthetic data often results in a domain gap when deployed to process real-world data, leading to poor performance due to disparities between synthetic and real images, and is resource-intensive for labeling real images.
Innovation Solution
A system preprocesses data to generate an intermediate representation that augments domain-invariant features, allowing neural networks to effectively process real-world data even when trained mainly on synthetic data, reducing the domain gap and resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If synthetic training data is used to train neural networks, then computational resource consumption and labeling costs are reduced, but domain gap between synthetic and real images increases leading to poor performance
Solution Approach 1:
The patent introduces a domain adaptation layer as an intermediary component between the synthetic data-trained network and real data processing. This layer acts as a mediator that transforms features from the synthetic domain to the real domain, reducing the domain gap while maintaining the benefits of synthetic data training. The adapter module learns to align feature distributions without requiring extensive real data for training.
Solution Approach 2:
The patent modifies network parameters dynamically during inference by introducing learnable transformation parameters that adapt the synthetic-trained network to real data characteristics. These parameter changes enable the model to adjust its feature representations to better match real-world distributions while retaining the computational efficiency of synthetic data training.
2Measurement precision
If human reviewers manually label real images, then training data quality and realism are improved, but labor cost and time consumption increase significantly
Solution Approach 1:
The patent implements a self-labeling mechanism where the neural network automatically generates labels for training data by processing unlabeled real images through its prediction capabilities. The system uses its own predictions as training labels, eliminating the need for human reviewers while maintaining continuous learning and improvement through self-correction and domain adaptation.
Solution Approach 2:
The patent creates synthetic copies of real images with automatically generated labels by rendering virtual scenes that replicate real-world scenarios. These synthetic copies serve as training data with perfect labels, avoiding the time-consuming manual labeling process while preserving the semantic accuracy needed for effective training.
3Ease of manufacture
If neural networks are trained solely on synthetic data, then data generation cost is reduced, but domain gap results in poor generalization to real-world scenarios
Solution Approach 1:
The patent performs preliminary domain adaptation training using a small set of real images before deploying the model for real-world applications. This preliminary action pre-adjusts the synthetic-trained network to real data characteristics, bridging the domain gap in advance while maintaining the ease and low cost of synthetic data generation for the majority of training.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing image data. One of the method includes receiving an input image from a source domain, the input image showing an object to be manipulated by a robot in a robotic process; processing the input image to generate an intermediate representation of the input image, comprising: generating a gradient orientation representation and a gradient magnitude representation of the input image; and generating the intermediate representation of the input image from the gradient orientation representation and the gradient magnitude representation; processing the intermediate representation of the input image using a neural network trained to make predictions about objects in images to generate a network output that represents a prediction about physical characteristics of the object in the input image.