Agent Navigation Training With Perspective-Invariant State Features
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Solution Overview
Problem
Training machine-learning models for agents, such as robots, is challenging when data is collected from agents with different perspectives and dynamics, particularly for legged robots that are hard to control and have limited operational time.
Innovation Solution
A computer-implemented method and system that uses a feature disentanglement model to extract perspective-invariant state features from sequential images collected by multiple cameras with different perspectives, allowing for imitation learning to train agents with varying visual perspectives and dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is collected from agents with different perspectives and dynamics, then training data diversity is improved, but training difficulty increases
Solution Approach 1:
The patent introduces a perspective transformation module as an intermediary that transforms visual observations from different perspectives into a unified representation space. This mediator enables data from agents with different perspectives and dynamics to be integrated without directly increasing training difficulty, as the transformation handles perspective differences systematically rather than requiring complex retraining for each perspective combination
Solution Approach 2:
The patent changes the parameter space by transforming observations from pixel-space to a normalized state-space representation. By adjusting the representation parameters through perspective transformation and state normalization, the system can accommodate diverse training data sources while maintaining consistent training conditions, thus improving adaptability without proportionally increasing training difficulty
2Measurement precision
If expert demonstration data is collected from legged robots, then training data quality is improved, but data collection time increases
Solution Approach 1:
The patent performs preliminary processing of expert demonstration data by extracting key state features and transforming them into a standardized representation format before actual model training begins. This preliminary action prepares the data in advance, allowing high-quality expert demonstrations to be efficiently utilized during training without requiring extensive processing time during the training phase itself
Solution Approach 2:
The patent extracts essential state features from expert demonstration data, separating the critical navigational information from redundant details. By taking out only the necessary state representations (positions, orientations, goal locations) rather than processing entire raw datasets, the system maintains training data quality while significantly reducing the time required to process and utilize expert demonstrations
3Loss of information
If multiple camera perspectives are used, then visual information completeness is improved, but computational complexity increases
Solution Approach 1:
The patent merges multiple camera perspectives by transforming them into a unified state representation that captures essential navigational information from all views. Instead of processing each camera feed independently through separate model instances, the system combines the information from multiple perspectives into a single integrated state representation, maintaining visual information completeness while reducing computational complexity through consolidation
Solution Approach 2:
The patent extracts the essential navigational state information from multiple camera perspectives, taking out only the critical features needed for navigation decisions. By extracting and retaining only the relevant state representations from each perspective rather than processing all visual details, the system maintains information completeness for navigation while reducing computational complexity by eliminating redundant processing
Data Source
AI summary
A computer-implemented method is disclosed for training one or more machine-learned models. The method can include inputting a first image frame and a second image frame into a feature disentanglement model and receiving, as an output of the machine-learned feature disentanglement model, a state feature and a perspective feature. The method can include inputting the state feature and the perspective feature into a machine-learned decoder model and receiving, as an output of the machine-learned decoder model, the reconstructed image frame. The method can include comparing the reconstructed image frame with a third image frame corresponding with the location and the perspective orientation. The method can include adjusting one or more parameters of the machine-learned feature disentanglement model based on the comparison of the reconstructed image frame and the third image frame.