Generator Neural Network for Sim-to-Real Robot Control Transfer
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in effectively training control policies for robotic agents using simulated data, as it often fails to generalize well to real-world environments due to differences in dynamics, appearance, and structure, leading to poor performance when applied to real-world robotic tasks.
Innovation Solution
A generator neural network is trained to adapt real-world images into canonical simulations, allowing the control policy to be trained in simulation and later applied to real-world scenarios, reducing the need for extensive real-world data and minimizing mechanical wear on robotic agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simulated training data is used to train control policy, then training efficiency is improved and mechanical wear is reduced, but generalization performance to real-world environments deteriorates
Solution Approach 1:
The patent creates a canonical simulation that copies the structure and appearance of the real-world environment with high fidelity. By training the control policy in this canonical simulation and then using a generator network to adapt images from the canonical simulation to match randomized simulations and real-world images, the system enables effective transfer of control policies from simulation to reality while maintaining training efficiency and reducing mechanical wear.
Solution Approach 2:
The patent employs parameter changes by systematically varying simulation parameters such as lighting conditions, camera angles, object positions, and material properties in the canonical simulation. The generator neural network learns to map between different parameter configurations, enabling the control policy to generalize across varying real-world conditions while being trained on simulated data.
2Measurement precision
If real-world training data is collected through actual robotic interaction, then training data quality is improved, but mechanical wear and resource consumption increase
Solution Approach 1:
The patent creates a canonical simulation that copies the structure and appearance of the real-world environment with high fidelity. By training the control policy in this canonical simulation and then using a generator network to adapt images from the canonical simulation to match randomized simulations and real-world images, the system enables effective transfer of control policies from simulation to reality while maintaining training efficiency and reducing mechanical wear.
Solution Approach 2:
The generator neural network serves as an intermediary that translates between the canonical simulation domain and the randomized simulation/real-world domain. This intermediary enables the control policy trained in simulation to effectively generalize to real-world scenarios without requiring extensive real-world training data collection, thereby reducing mechanical wear while maintaining training data quality.
3Manufacturing precision
If a canonical simulation is used without randomization, then simulation accuracy is improved, but adaptability to real-world variations deteriorates
Solution Approach 1:
The patent segments the simulation training process into two distinct components: a canonical simulation that provides accurate ground truth data with known parameters, and a randomized simulation that introduces variations to improve generalization. The generator neural network bridges these two segments, learning to map from the accurate canonical simulation to the varied randomized simulation, thereby achieving both accuracy and adaptability.
Solution Approach 2:
The patent introduces dynamics by allowing the simulation environment to vary through randomization of parameters such as lighting, camera angles, and object properties. The generator neural network learns to adapt to these dynamic variations by training on pairs of canonical and randomized simulation images, enabling the control policy to generalize to real-world variations while maintaining the accuracy benefits of canonical simulation.
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.