Hand-Held Manipulator Video Localization for Training Data Yield
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Solution Overview
Problem
Existing methods for training robots through imitation learning using hand-held gripper videos face challenges in localizing every video of tasks performed, leading to inefficiencies in generating effective training data.
Innovation Solution
A method involving a hand-held manipulation device with a camera that captures task videos, generates a scene map, localizes the device in the scene, and stores videos for training data only if localization is successful, discarding non-localizable videos to improve yield and provide feedback for corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all demonstration videos are collected for training data, then the quantity of training data increases, but the quality and effectiveness decrease due to inclusion of non-localizable videos
Solution Approach 1:
The patent applies local quality by differentiating between localizable and non-localizable videos within the training dataset. Instead of treating all videos uniformly, the system identifies and separates videos based on their localizability quality attribute, ensuring that only high-quality localizable videos are used for training, thereby maintaining data quality while managing quantity effectively
Solution Approach 2:
The patent implements feedback by determining whether each video can be localized and using this information to decide whether to include it in the training dataset. The localization determination acts as a feedback mechanism that filters videos, providing quality control and ensuring that only videos meeting the localizability criterion are stored as training data
2Reliability
If yield checking is implemented to filter videos, then the quality of training data improves, but the time and computational resources required increase
Solution Approach 1:
The patent applies preliminary action by performing localization determination on videos before they are stored as training data. This pre-filtering process ensures that only videos that can be successfully localized are saved, preventing waste of storage space and future processing time on non-localizable videos. The yield checking is done upfront rather than during subsequent training processes
3Measurement precision
If comprehensive video analysis is performed to determine localizability, then the accuracy of training data selection improves, but the complexity of the processing system increases
Solution Approach 1:
The patent extracts the essential criterion for video selection - localizability - from the complex task of comprehensive video analysis. By focusing specifically on whether a video can be localized rather than performing exhaustive analysis of all video attributes, the system achieves accurate video selection while maintaining relatively simple processing requirements. Only the necessary localization check is performed, not comprehensive analysis of all possible video characteristics
Data Source
AI summary
A method includes receiving a mapping video of a scene; generating a map of the scene based on the mapping video; receiving a plurality of demonstration videos of a hand-held manipulation device performing one or more tasks in the scene; for each video among the plurality of demonstration videos, determining whether the hand-held manipulation device can be localized in the scene based on the video and the map; and for each video among the plurality of demonstration videos for which the hand-held manipulation device can be localized in the scene, localizing the hand-held manipulation device in the scene based on the video and the map, and storing the video as training data.