Feature-Level Adaptation for Vision-Based Robot Action Models
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Solution Overview
Problem
Existing machine learning-based robotic control methods face a significant 'reality gap' between simulated and real-world environments, leading to inefficiencies and potential safety concerns, as they require extensive use of physical robots for training data, consume resources, and may not accurately reflect real-world conditions.
Innovation Solution
Implement feature-level domain adaptation using embedding consistency losses and action consistency losses to train a vision-based robotic action model, ensuring that features and actions generated from simulated and real images, as well as distorted versions, are similar, thereby mitigating the reality gap and enhancing model accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simulated data is used for training, then resource consumption and time are reduced, but the reality gap between simulated and real environments causes decreased model accuracy
Solution Approach 1:
The patent uses simulated environments as copies of real environments for training, but applies domain adaptation techniques to make the simulated copies more faithful representations. This allows efficient training on simulated data while improving the transferability to real-world scenarios through targeted adaptations of simulation parameters and physics models.
Solution Approach 2:
The patent systematically varies simulation parameters (lighting conditions, textures, physics properties, sensor noise characteristics) to bridge the reality gap. By adjusting these parameters to better match real-world conditions, the trained models achieve higher accuracy when deployed on physical robots without sacrificing training efficiency.
2Reliability
If extensive real-world data collection is performed, then model accuracy improves, but resource consumption, time, and safety risks increase
Solution Approach 1:
The patent replaces expensive and time-consuming real-world data collection with simulated data generation. By creating virtual copies of real-world scenarios in simulation environments, the system achieves comparable model accuracy without the associated resource consumption, time costs, and safety risks of physical robot operation.
Solution Approach 2:
The patent performs comprehensive model training and validation in simulated environments before deploying to real robots. This preliminary action in simulation allows the model to be thoroughly tested and optimized in advance, reducing the need for extensive real-world trial-and-error data collection and minimizing resource consumption during actual deployment.
3Adaptability or versatility
If domain randomization is applied, then model robustness improves, but manual parameter definition complexity increases
Solution Approach 1:
The patent implements automated domain adaptation pipelines that self-adjust simulation parameters based on real-world data characteristics. Instead of requiring manual definition of randomization parameters, the system automatically learns and applies appropriate parameter distributions, reducing configuration complexity while maintaining model robustness through systematic exploration of parameter spaces.
Data Source
AI summary
Implementations disclosed herein relate to mitigating the reality gap through feature-level domain adaptation in training of a vision-based robotic action machine learning (ML) model. Implementations mitigate the reality gap through utilization of embedding consistency losses and/or action consistency losses during training of the action ML model.