Semantic Image Augmentation for Sim-to-Real Robot Policy Training
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Solution Overview
Problem
Conventional approaches for training policy models to control robots fail to adapt to real-world scenarios due to the 'sim-to-real' or 'real-to-real' gap, where differences in image characteristics such as colors, textures, and lighting conditions lead to incorrect robot control, and the conversion of real-world images into canonical images often results in inaccurate depth information, causing collision and grasping failures.
Innovation Solution
A computer-implemented method using a trained image generative model that generates augmented images conditioned on input images, depth, and semantic information, along with text describing augmentations, to train machine learning models like policy models, preserving depth and semantic information for effective robot control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If training data is generated via simulation in a virtual environment, then training time is reduced and robot safety is improved, but the policy model fails to adapt to real-world scenarios due to differences in image characteristics
Solution Approach 1:
The patent creates a canonical image that copies and preserves critical geometric information (depth map, semantic segmentation) from real-world images while allowing flexible rendering of appearance properties. This canonical representation serves as an idealized template that bridges simulation and reality by maintaining structurally accurate depth and semantic information that can be rendered in simulation environments.
Solution Approach 2:
The patent changes the parameter representation by separating geometric parameters (depth, semantics) from appearance parameters (color, texture, lighting). This allows independent optimization: geometric parameters are preserved accurately from real images, while appearance parameters can be varied in simulation to match different real-world conditions, thereby improving adaptability without requiring extensive real-world training.
2Productivity
If real-world images are converted into canonical images, then training efficiency is improved, but depth information becomes inaccurate leading to collision and grasping failures
Solution Approach 1:
The patent explicitly copies the depth map and semantic segmentation map from the real-world input image into the canonical image representation. This ensures that the geometric information is preserved with high fidelity, maintaining accurate depth measurements while enabling efficient training through the standardized canonical format.
Solution Approach 2:
The patent segments the image processing into distinct components: depth map extraction, semantic segmentation, and appearance rendering. By separating these functions, the system can preserve accurate depth information from the original image while independently processing appearance properties, thus maintaining measurement precision for depth while achieving training efficiency through modular processing.
3Adaptability or versatility
If captured images are transformed into canonical images with simplified representations, then generalization capability is improved, but objects appear at different depths causing control failures
Solution Approach 1:
The patent copies the depth map from the captured image to the canonical image, ensuring that depth information is preserved accurately. This allows the canonical representation to maintain correct spatial relationships and depth measurements while still providing simplified and standardized appearance properties that improve generalization to different scenarios.
Solution Approach 2:
The patent changes the representation parameters by decoupling depth information from appearance properties. The depth map is preserved as an accurate geometric representation, while appearance parameters (colors, textures, lighting) are transformed into a canonical form that improves generalization. This parameter separation allows the system to maintain measurement precision for depth while achieving versatility in handling diverse visual conditions.
Data Source
AI summary
A computer-implemented technique for training machine learning models includes processing one or more input images using a trained image generative model to generate one or more augmented images, where the trained image generative model generates each augmented image included in the one or more augmented images conditioned on an input image included in the one or more input images, depth information associated with the input image, semantic information associated with the input image, and text describing an augmentation to make to the input image; and performing, based on the one or more augmented images, one or more operations to train an untrained machine learning model to generate a trained machine learning model.