Vision Foundation Model Distillation for Efficient Robot Learning
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Solution Overview
Problem
Existing vision foundation models (VFMs) are either task-specific and lack universality or are off-the-shelf models that underperform in robot learning tasks, and existing efforts to improve their general applicability have yielded mixed results.
Innovation Solution
A compact AI-based VFM is developed by distilling capabilities from multiple large VFMs, using a visual encoder and feature translators, with a combination of cosine and smooth-L1 loss functions, to integrate diverse visual representations, enhancing computational efficiency and performance across various tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple large vision foundation models are used to improve visual representation quality, then the model capability and accuracy are improved, but the computational complexity and training costs increase
Solution Approach 1:
The patent segments the knowledge from multiple large vision foundation models into discrete encoded tokens representing visual features. Instead of using all large models simultaneously, the system divides their capabilities into extractable token units that can be selectively integrated, reducing computational complexity while preserving visual representation quality.
Solution Approach 2:
The patent creates a compact distilled model that copies essential visual representation capabilities from multiple large vision foundation models. By training a smaller model to replicate the key functions of larger models through knowledge distillation, the system achieves similar accuracy with reduced computational requirements.
2Adaptability or versatility
If multiple different vision foundation models are integrated to improve task versatility, then the adaptability across tasks is improved, but the model integration complexity increases
Solution Approach 1:
The patent creates a universal compact distilled model that can perform multiple downstream tasks across different domains. By designing a single model architecture capable of learning diverse visual representations from multiple source models, the system achieves task versatility without requiring separate specialized models for each task.
Solution Approach 2:
The patent introduces encoded tokens as an intermediary representation layer between multiple vision foundation models and downstream tasks. These tokens serve as a common language that facilitates integration of diverse model outputs, simplifying the integration process while maintaining adaptability across different task types.
3Productivity
If a compact distilled model is created to reduce computational costs, then the training efficiency is improved, but the model size and complexity are reduced
Solution Approach 1:
The patent employs a compact distilled model that serves as a lightweight alternative to large vision foundation models. This smaller model requires fewer computational resources for training and deployment, making it more efficient and accessible despite having reduced model size compared to the source models.
Data Source
AI summary
Images are received by an AI based vision foundation model (VFM) as input. Encoded tokens are generated using the received images by a first component of the AI based VFM. Each encoded token includes a spatial token corresponds to a respective image patch of a set of image patches of at least one image. Additional encoded tokens are extracted from a set of additional AI based VFMs by a second component of the AI based VFM. The additional encoded tokens represent visual data specific to at least one of the additional AI based VFM. Each additional AI based VFM is independent of and different from the AI based VFM. Each additional encoded token is matched to a respective encoded token generated by the first component of AI based VFM using the second component of the AI based VFM.