Generator Neural Network Adaptation for Sim-to-Real Robot Control
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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 system that trains a generator neural network to adapt real-world images into canonical simulations, allowing the control policy to be trained in simulation and later applied to real-world environments, 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 and data availability are improved, but generalization performance to real-world environments deteriorates
Solution Approach 1:
The patent creates a canonical simulation that copies the essential structure and dynamics of the real-world environment. By training the control policy in this copied simulation environment and then transferring it to the real world, the system achieves both efficient simulated training and good real-world generalization performance.
Solution Approach 2:
The patent modifies simulation parameters to create a canonical version that better matches real-world characteristics. By adjusting parameters such as physics dynamics, sensor models, and environmental properties in the simulation, the trained policy generalizes more effectively to real-world deployment.
2Reliability
If real-world training data is collected through actual interaction, then data quality and realism are improved, but time consumption and mechanical wear increase
Solution Approach 1:
The patent performs preliminary training in simulation before deploying to the real world. By pre-training the control policy in the canonical simulation environment, the system accumulates extensive training experience without actual mechanical wear or time consumption associated with real-world interaction.
Solution Approach 2:
The patent introduces canonical simulation as an intermediary between theoretical control algorithms and real-world deployment. This intermediary environment provides realistic training conditions without the costs of actual physical interaction, bridging the gap between simulation and reality.
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.