Robotic Task Training Data Generation Through Video Scenario Variation
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Solution Overview
Problem
Training robotic devices to perform tasks is ineffective without sufficient training data, which is often expensive and time-consuming to collect, especially when objects and environments vary across tasks.
Innovation Solution
A system generates new training data by modifying existing demonstrations of a robotic device's tasks, segmenting them into subtasks, and adapting object locations and trajectories to simulate new scenarios, using neural networks and simulation techniques to create diverse training scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sufficient training data is collected to improve robot task performance accuracy, then task accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent creates synthetic training data by copying and modifying existing demonstration videos through digital manipulation techniques including frame extraction, object segmentation, and scenario transformation. This allows generation of abundant training data without physical re-demonstration, resolving the contradiction between data quantity and time consumption
Solution Approach 2:
The system transforms existing demonstrations by changing key parameters such as object locations, trajectories, and environmental conditions through digital modification. This enables creation of diverse training scenarios from limited source data, improving task accuracy while avoiding the time cost of collecting new demonstrations
2Reliability
If sufficient training data is collected to improve robot task performance accuracy, then task accuracy is improved, but cost increases significantly
Solution Approach 1:
The patent creates synthetic training data by copying and modifying existing demonstration videos through digital manipulation techniques including frame extraction, object segmentation, and scenario transformation. This allows generation of abundant training data without physical re-demonstration, resolving the contradiction between data quantity and time consumption
Solution Approach 2:
The system replaces physical data collection mechanisms (human demonstration, video recording equipment, physical manipulation) with computational methods including neural network-based object segmentation, digital frame manipulation, and algorithmic scenario generation. This substitution dramatically reduces cost while maintaining data quality
3Adaptability or versatility
If training data covers varied objects and environments to improve robot adaptability, then adaptability is improved, but data collection complexity increases
Solution Approach 1:
The patent segments demonstrations into object-centric units using neural network-based object segmentation to identify and separate individual objects from background. This enables independent manipulation and recombination of objects in synthetic scenarios, allowing coverage of diverse objects and environments through systematic composition rather than exhaustive collection
Solution Approach 2:
The system transforms existing demonstrations by changing key parameters such as object locations, trajectories, and environmental conditions through digital modification. This enables creation of diverse training scenarios from limited source data, improving task accuracy while avoiding the time cost of collecting new demonstrations
Data Source
AI summary
Apparatuses, systems, and techniques to generate data to train a robotic device to perform tasks. In at least one embodiment, one or more first videos of a robotic device performing a task is used to generate one or more second videos of the robotic device performing the task differently than depicted in the one or more first videos.