Robot Learning Cache for Real-Time Fleet Knowledge Sharing
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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 time-consuming and computationally intensive, preventing real-time adaptation and fleet-wide knowledge dissemination.
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 updating of machine learning models periodically, enabling near-instantaneous learning and fleet-wide knowledge sharing without the need for extensive retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are updated frequently to incorporate new information, then robot learning speed and adaptability improve, but computational time and processing resources increase significantly
Solution Approach 1:
The patent segments the learning system into two parts: a static machine learning model that remains unchanged, and a dynamic local cache that stores new information separately. This allows the system to incorporate new information rapidly in the cache without triggering computationally intensive model retraining, thus improving learning speed while avoiding excessive computational overhead.
Solution Approach 2:
The local cache acts as an intermediary between new information and the machine learning model. Instead of directly updating the model with new information, the system stores representations in the cache, which then serves as a quick-access repository that enhances model performance without requiring full model retraining.
2Adaptability or versatility
If machine learning models are updated frequently to incorporate new information, then robot learning speed and adaptability improve, but computational resources and processing power increase significantly
Solution Approach 1:
The system separates the computationally heavy machine learning model from the lightweight local cache. New information is stored as simple representations in the cache rather than being processed through the full model training pipeline, dramatically reducing the computational power required for rapid learning.
Solution Approach 2:
The patent uses inexpensive, simple data structures (embeddings and classifications) in the local cache instead of maintaining complex trained models. These cache entries require minimal processing power to store and retrieve, enabling rapid learning without significant computational resource consumption.
3Adaptability or versatility
If learned information is shared across the entire robot fleet, then fleet-wide knowledge dissemination improves, but communication bandwidth and data transmission time increase
Solution Approach 1:
The patent extracts only the essential elements of learned information (embeddings and classifications) for sharing across the fleet, rather than transmitting complete models or raw data. This extraction approach enables effective knowledge dissemination while minimizing communication bandwidth requirements and data transmission time.
4Measurement precision
If complete machine learning models are redistributed to robots, then model accuracy and performance improve, but update time and communication overhead increase significantly
Solution Approach 1:
Instead of redistributing complete machine learning models, the system creates and distributes simplified copies in the form of embeddings and classifications stored in local caches. These lightweight copies capture the essential learned information and can be rapidly transmitted and applied, maintaining performance benefits while dramatically reducing update time.
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.