Inverse Dynamics Pseudo-Labeling for Automatic Interface Actions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning systems lack efficiency and are costly due to the reliance on unlabeled and noisy datasets, making it difficult to automate tasks like navigating websites or booking flights without sufficient large, labeled datasets.
Innovation Solution
A method involving unlabeled digital video data generation of pseudo-labels using an inverse dynamics model (IDM) to mimic user actions, followed by training a machine learning model to perform actions via a user interface without human intervention, utilizing both past and future information within the video data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If extant machine learning methods are used to train models without explicit action labels, then the system can utilize unlabeled data, but the training process becomes susceptible to exploration bottlenecks and lacks efficiency
Solution Approach 1:
The patent introduces an inverse dynamics model as an intermediary component that generates pseudo-labels from unlabeled video data. This mediator bridges the gap between unlabeled data and the labeled data requirement of behavioral cloning, enabling efficient training without manual labeling while avoiding exploration bottlenecks.
Solution Approach 2:
The system performs preliminary action by pre-training the inverse dynamics model on available labeled data before using it to generate pseudo-labels for unlabeled data. This preliminary training enables the model to make informed predictions that mimic human actions, creating a foundation for subsequent efficient training on large datasets.
2Measurement precision
If manual labeling of large datasets is performed to improve training quality, then measurement precision increases, but the process becomes expensive and complicated
Solution Approach 1:
The system implements self-service by enabling the inverse dynamics model to automatically generate its own training labels from unlabeled video data. This self-labeling capability eliminates the need for manual human annotation, reducing both cost and complexity while maintaining high accuracy through the model's learned understanding of human actions.
Solution Approach 2:
The patent uses copying by creating pseudo-labels that replicate the structure and quality of manually labeled data. The inverse dynamics model generates these synthetic labels by simulating human action sequences, providing accurate training signals without requiring actual human annotators to manually create each label.
3Adaptability or versatility
If exploration-based methods are used to learn without explicit labels, then adaptability improves, but the system encounters exploration bottlenecks and requires extensive time
Solution Approach 1:
The system implements feedback by using the inverse dynamics model to generate pseudo-labels that provide directional guidance during training. This feedback mechanism allows the behavioral cloning model to learn from unlabeled data efficiently, combining the adaptability of unsupervised learning with the time efficiency of supervised learning by providing meaningful training signals without manual labels.
Data Source
AI summary
Disclosed herein are methods, systems, and computer-readable media for training a machine learning model to label unlabeled data and/or perform automated actions. In an embodiment, a method comprises receiving unlabeled digital video data, generating pseudo-labels for the unlabeled digital video data, the generating comprising receiving labeled digital video data, training an inverse dynamics model (IDM) using the labeled digital video data, and generating at least one pseudo-label for the unlabeled digital video data, wherein the at least one pseudo-label is based on a prediction, generated by the IDM, of one or more actions that mimic at least one timestep of the unlabeled digital video data. In some embodiments, the method further comprises adding the at least one pseudo-label to the unlabeled digital video data and further training the IDM or a machine learning model using the pseudo-labeled digital video data.