Robot Embedding Cache for Real-Time Fleet Learning Updates
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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 to new objects and situations.
Innovation Solution
Robots can store new information in a local cache for immediate use and share embeddings of learned information with other robots, allowing for rapid dissemination and utilization 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 accuracy and adaptability improve, but computational time and processing resources increase significantly
Solution Approach 1:
The patent segments the learning process into two distinct components: (1) a lightweight local cache that stores embedding representations for immediate use, and (2) a periodic full model update process. This segmentation allows robots to access new information instantly through the cache without waiting for computationally intensive model retraining, thus resolving the contradiction between fast adaptability and time-consuming updates.
Solution Approach 2:
The patent creates simplified copies of the machine learning model's knowledge in the form of embedding representations stored in a local cache. These embeddings are lightweight copies that can be quickly retrieved and applied without retraining the full model, enabling rapid adaptation while avoiding the time cost of frequent model updates.
2Measurement precision
If machine learning models are retrained to incorporate new information, then learning accuracy improves, but computational resources and processing power increase
Solution Approach 1:
The patent extracts the essential knowledge from the machine learning model in the form of embedding representations and stores them separately in a local cache. This extraction allows the system to maintain high learning accuracy by having access to updated knowledge without requiring the full computational power needed for model retraining, as only the lightweight embeddings need to be updated and stored.
3Productivity
If information is shared across the robot fleet continuously, then fleet-wide learning speed improves, but communication bandwidth and data transmission requirements increase
Solution Approach 1:
The patent transmits only embedding representations (lightweight copies of knowledge) rather than full model parameters or raw training data. This copying approach enables continuous fleet-wide sharing of learned information at high speed while minimizing communication bandwidth requirements, as the embeddings are compact data structures that can be quickly transmitted and integrated into local caches.
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable media storing executable instructions, for enhancing robot learning. In some implementations, a robot stores first embeddings generated using a first machine learning model, and the first embeddings include one or more first private embeddings that are not shared with other robots. The robot receives a second machine learning model from a server system over a communication network. The robot generates a second private embedding for each of the one or more first private embeddings using the second machine learning model. The robot adds the second private embeddings to the cache of the robot and removes the one or more first private embeddings from the cache of the robot.