Autonomous Robot Training Data Augmentation for Environment Variety
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robot training data methods require significant resources and time to capture varied environments for robust machine learning, leading to inefficiencies and potential errors due to limited training data scenarios.
Innovation Solution
The method generates augmented environment instances by applying various augmentations to context data, such as adding virtual objects or altering environmental aspects, to create diverse training scenarios efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If real-world environment capture methods are used to generate training data, then training data represents actual environmental conditions, but it requires significant resources and time and is limited in variety
Solution Approach 1:
The system creates virtual copies of real-world environments through 3D modeling and simulation. Instead of physically capturing every possible environment scenario, the system generates synthetic environment instances that replicate real-world conditions, allowing unlimited replication without additional physical capture time.
Solution Approach 2:
The system modifies environmental parameters (lighting conditions, object positions, weather, time of day) within the virtual environment to create diverse training scenarios. This allows generating unlimited variations of the same environment without physically recapturing each variation.
2Adaptability or versatility
If real-world environment capture methods are used to generate training data, then training data represents actual environmental conditions, but it is limited in variety and requires significant resources
Solution Approach 1:
The system uses virtual environment copies that can be endlessly varied through software parameters rather than physical capture systems. This provides unlimited environmental variety while reducing the complexity of hardware requirements to basic sensors and modeling tools.
Solution Approach 2:
The system separates the environment representation from the physical capture process by using modular 3D environment models. Each environment can be independently modeled and then varied through parameter changes, decoupling the complexity of capture systems from the need for diverse training data.
3Reliability
If limited training data scenarios are used, then data capture is simpler and faster, but it leads to potential errors and reduced robustness in robot learning
Solution Approach 1:
The system generates multiple copies of the same environment scenario with different parameters (lighting, objects, positions) to create diverse training data without additional capture cost. This maintains high reliability through varied scenarios while keeping productivity high through efficient virtual generation.
Solution Approach 2:
The system performs preliminary environment modeling and creation before actual robot training. By pre-generating diverse environmental scenarios in the virtual domain, the system ensures robust training data is available before physical deployment, improving reliability without slowing down the overall productivity.
Data Source
AI summary
Systems, methods, and computer program products for generating training data are described. Action data and context data are recorded for a robot body performing an action or task in an environment. The context data is augmented virtually to include variations from the recorded environment while the action data remains unchanged, and instances of training data are generated including the augmentations, to produce a large and varied training data set.