Generator Neural Network for Sim-to-Real Robot Domain Adaptation
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Solution Overview
Problem
Existing robotic control systems face challenges in effectively transitioning from simulated environments to real-world scenarios due to domain differences, leading to poor performance and mechanical wear, as real-world data collection is resource-intensive and noisy.
Innovation Solution
A generator neural network is trained to adapt real-world images into canonical simulations, allowing control policies to be trained in simulation and later applied in real-world environments, reducing the need for real-world data and mechanical wear.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world training data is collected through actual interaction of the robotic agent with the real-world environment, then the training data reflects accurate real-world conditions, but the process is time-intensive, resource-intensive, causes mechanical wear, and produces noisy labels
Solution Approach 1:
The patent creates a simulated environment that copies the real-world environment's dynamics, semantics, and appearance. This virtual copy allows training data to be generated without physical interaction, eliminating time loss and mechanical wear while maintaining training effectiveness through domain adaptation techniques
Solution Approach 2:
A domain adapter neural network is introduced as an intermediary between the simulated environment and the real-world robot. This adapter learns to translate simulated observations into real-world equivalents, bridging the domain gap and enabling transfer of control policies without direct real-world training
2Ease of manufacture
If a simulation is used to generate training data, then the data can be generated easily without mechanical wear, but the simulation training data is systematically different from real-world data, resulting in poor performance when applied to real-world robotic control
Solution Approach 1:
The domain adapter neural network dynamically adjusts parameters by learning a transformation function between simulation and real-world domains. This parameter adaptation allows the control policy to generalize from simulated training data to real-world execution, resolving the systematic differences between the two domains
Solution Approach 2:
The training approach combines simulated training data with a domain adapter neural network to create a composite training system. This composite approach leverages the ease of simulated data generation while compensating for domain differences through the learned adapter, achieving both ease of manufacture and reliability
3Measurement precision
If domain adaptation is performed using real-world images, then the adaptation is accurate to real-world conditions, but real-world images are unavailable or difficult to obtain
Solution Approach 1:
Instead of adapting simulation to match real-world images (which are unavailable), the patent inverts the approach by training the domain adapter to map real-world images to simulated images. This inversion allows the adapter to learn the domain transformation using only simulated data, making the process feasible without real-world images while maintaining adaptation accuracy
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a generator neural network to adapt input images.