Robot Embedding Sharing for Real-Time Fleet Learning
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Solution Overview
Problem
Current robot systems face challenges in efficiently learning and sharing new information, as updating machine learning models is computationally intensive and time-consuming, preventing real-time adaptation and fleet-wide knowledge dissemination.
Innovation Solution
Robots can store learned information in a local cache for immediate use and share embeddings with other robots, allowing rapid dissemination of new knowledge without retraining models, while a server periodically updates machine learning models based on aggregated learning from the fleet.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are updated frequently to incorporate new information, then robot learning capability and adaptability improve, but computational time and resources increase significantly
Solution Approach 1:
The patent segments the learning process into two distinct components: (1) embedding generation and storage in local cache, and (2) periodic machine learning model updates. This segmentation allows robots to immediately store and use learned information in cache without waiting for computationally intensive model retraining, while models are updated periodically in the background. The embedding representations serve as lightweight proxies that enable rapid adaptation without full model updates.
Solution Approach 2:
The patent performs preliminary action by pre-computing and storing embedding representations of learned information in local cache before model updates occur. These pre-computed embeddings allow robots to immediately apply new knowledge to object recognition tasks without waiting for the lengthy model retraining process to complete. The embeddings are prepared in advance and made readily available for inference.
2Measurement precision
If machine learning models are retrained to incorporate new information, then model accuracy improves, but computational complexity and energy consumption increase
Solution Approach 1:
The patent creates simplified copies of learned information in the form of embedding representations stored in local cache. These embedding copies capture the essential features of learned objects and can be used for rapid comparison and recognition without requiring the full machine learning model to be retrained. The embeddings serve as lightweight proxies that consume minimal computational energy while maintaining recognition accuracy.
Solution Approach 2:
The patent changes the representation parameters from full model weights to compact embedding vectors. Instead of updating the entire machine learning model which requires substantial computational resources, the system stores and compares condensed embedding representations that require minimal energy. This parameter transformation enables efficient information storage and retrieval with significantly reduced computational overhead.
3Adaptability or versatility
If learned information is shared across the robot fleet through model updates, then fleet-wide knowledge dissemination improves, but communication overhead and update frequency requirements increase
Solution Approach 1:
The patent extracts only the essential learned information in the form of embedding representations from the full machine learning model. These extracted embeddings are stored in local cache and can be shared across the robot fleet through communication. By taking out only the necessary embedding data rather than transmitting entire models or large datasets, the system achieves fleet-wide knowledge dissemination with minimal communication overhead.
Solution Approach 2:
The patent applies partial action by sharing only the relevant embedding representations rather than complete model updates. Robots exchange selective embedding data that represents specific learned objects or concepts, performing just enough knowledge sharing to improve fleet capability without the excessive overhead of comprehensive model synchronization. This partial information exchange is sufficient for achieving fleet-wide adaptability.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sharing learned information among robots. In some implementations, a robot obtains sensor data indicating characteristics of an object. The robot determines a classification for the object and generates an embedding for the object using a machine learning model stored by the robot. The robot stores the generated embedding and data indicating the classification for the object. The robot sends the generated embedding and the data indicating the classification to a server system. The robot receives, from the server system, an embedding generated by a second robot and a corresponding classification. The robot stores the received embedding and the corresponding classification in the local cache of the robot. The robot may then use the information in the cache to identify objects.