Policy Neural Network Adaptation for Sim-to-Real Robotic Control
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Solution Overview
Problem
Existing robotic control systems face challenges in effectively transitioning from simulated training data to real-world environments due to domain differences, leading to poor performance, as real-world data collection is resource-intensive and noisy.
Innovation Solution
A policy neural network is trained using a combination of labeled simulated data and a small amount of unlabeled real-world data, employing a self-supervised objective and task-specific objectives to adapt to real-world conditions without requiring extensive labeled real-world data, reducing mechanical wear and improving generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simulated training data is used to train the policy neural network, then the amount of training data increases and training efficiency improves, but the performance of the robotic agent in the real-world environment deteriorates due to domain differences
Solution Approach 1:
The patent introduces a domain adaptation layer as an intermediary component between the simulated training domain and the real-world deployment domain. This layer learns to transform features or representations from the simulation domain to match the real-world domain, allowing the network to benefit from abundant simulated data while adapting to real-world conditions. The domain adaptation layer acts as a bridge that reconciles the domain differences without requiring extensive retraining on real-world data.
Solution Approach 2:
The patent performs domain adaptation preprocessing during the training phase by introducing domain adaptation objectives and auxiliary networks that learn to align simulated and real-world distributions before the final deployment. This preliminary alignment work is done in advance using available simulated data and a small subset of real-world data, so that when the robotic agent is deployed in the real world, the domain gap has already been reduced, improving transfer performance.
2Measurement precision
If real-world training data is collected through actual interaction, then the training data reflects real conditions, but the process is time-intensive, resource-intensive, and causes mechanical wear on the robotic agent
Solution Approach 1:
The patent creates a simulated copy or virtual replica of the real-world environment and robotic agent dynamics. This simulation environment replicates the key physical properties, sensor characteristics, and task conditions of the real world. By training in this copied virtual environment, the system obtains abundant training data without the time and resource costs of collecting data from actual physical interactions, while still maintaining sufficient fidelity to prepare the agent for real-world deployment.
3Quantity of substance
If real-world training data is collected, then the training data is obtained, but the labels are noisy and the data quality is reduced
Solution Approach 1:
The patent introduces domain adaptation networks and alignment mechanisms as intermediaries that filter and clean the training signals. These adaptation layers learn to identify and correct noisy or inconsistent labels by comparing patterns across multiple data sources and using the structured information from simulated environments as a reference. This intermediary processing step reduces the impact of label noise while still utilizing the valuable real-world data.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a policy neural network for use in controlling a real-world agent in a real-world environment. One of the methods includes training the policy neural network by optimizing a first task-specific objective that measures a performance of the policy neural network in controlling a simulated version of the real-world agent; and then training the policy neural network by jointly optimizing (i) a self-supervised objective that measures at least a performance of internal representations generated by the policy neural network on a self-supervised task performed on real-world data and (ii) a second task-specific objective that measures the performance of the policy neural network in controlling the simulated version of the real-world agent.