Vision-Based Sim2Real Training for Robot Reality Gap Mitigation
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Solution Overview
Problem
Existing robotic control methods using machine learning models face challenges due to a significant 'reality gap' between simulated and real environments, leading to task-agnostic policies that fail to accurately adapt to real-world conditions, often requiring extensive real-world data and resource-intensive training on physical robots.
Innovation Solution
A simulation-to-real (Sim2Real) model is trained using a vision-based robot task model, such as a reinforcement learning neural network, to generate predicted real images tailored to specific robotic tasks, incorporating adversarial and cycle consistency losses to bridge the reality gap and improve model performance on real robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If simulated training data is used to train machine learning models, then training time and resource consumption are reduced, but the reality gap between simulated and real environments causes degraded model performance on real robots
Solution Approach 1:
The patent creates a simulated training environment that copies real-world physics and sensor characteristics to generate training data. By carefully designing the simulation to replicate real robot dynamics, sensor noise models, and environmental conditions, the system produces synthetic training data that transfers effectively to real robots without requiring extensive real-world data collection
Solution Approach 2:
The patent systematically varies simulation parameters such as lighting conditions, object positions, robot velocities, and sensor noise levels to create diverse training scenarios. This parameter randomization within the simulation ensures the trained model robustness while maintaining the reality gap mitigation through consistent physics models
2Measurement precision
If real-world physical robots are used to generate training data, then training data accuracy is improved, but time consumption and resource usage increase significantly
Solution Approach 1:
The simulation engine creates virtual copies of the real robot system with matched dynamics models, sensor characteristics, and environmental properties. These digital twins generate training data that mirrors real-world conditions without requiring physical robot operation, dramatically reducing data collection time while maintaining accuracy
Solution Approach 2:
The system performs preliminary modeling and validation to ensure the simulation accurately represents the real system before generating training data. By pre-configuring the simulation with correct physics parameters, sensor noise models, and environmental conditions, the system eliminates the need for time-consuming real-world data collection while maintaining data fidelity
3Illumination intensity
If GAN models are used for image-to-image translation between simulated and real environments, then visual realism is improved, but task-agnostic adaptation removes important semantics and styles
Solution Approach 1:
The system performs preliminary task-specific feature extraction and preservation before applying visual domain adaptation. By identifying and protecting task-critical semantics such as object identities, robot poses, and spatial relationships during the translation process, the system maintains both visual realism and task-relevant information
Solution Approach 2:
The patent applies different adaptation strategies to different regions and features of the images. Task-critical features such as object boundaries, robot configurations, and semantic markers are preserved with high fidelity, while non-critical visual aspects like lighting conditions and textures are adapted to match real-world appearance
Data Source
AI summary
Implementations disclosed herein relate to mitigating the reality gap through training a simulation-to-real machine learning model (“Sim2Real” model) using a vision-based robot task machine learning model. The vision-based robot task machine learning model can be, for example, a reinforcement learning (“RL”) neural network model (RL-network), such as an RL-network that represents a Q-function.