Robot Learning Evaluation Using Shared Embedding Caches
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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 new information in a local cache for immediate use and share embeddings of learned information across a fleet, allowing rapid dissemination of new knowledge without retraining models, while a server periodically updates machine learning models based on aggregated learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are updated frequently to incorporate new robot learning, then the robots can adapt to new objects and situations more quickly, but the computational cost and time required for model training increase significantly
Solution Approach 1:
The patent segments the learning process into two distinct components: (1) generating embeddings from sensor data using a pre-trained machine learning model, and (2) storing these embeddings in a cache for immediate use. This segmentation allows robots to bypass the time-consuming model training step while still incorporating new learning, as the embedding generation can be performed quickly on individual robots without requiring full model retraining.
Solution Approach 2:
The machine learning model is pre-trained offline before deployment to the robots. This preliminary action transfers the computationally intensive training work to a centralized system, allowing the robots to perform only lightweight inference tasks (generating embeddings from sensor data) during operation. The pre-trained model serves as a foundation that enables rapid adaptation without requiring time-consuming retraining on each robot.
2Adaptability or versatility
If machine learning models are updated frequently to incorporate new robot learning, then the robots can adapt to new objects and situations more quickly, but the computational resources and energy consumption increase
Solution Approach 1:
The patent segments the computational workload by separating the heavy model training task from the lightweight embedding generation task. The energy-intensive training is performed once offline, while the robots only perform low-energy inference to generate embeddings from sensor data. This segmentation dramatically reduces the energy consumption required for each robot to adapt to new objects, as they no longer need to perform computationally expensive training operations.
Solution Approach 2:
Instead of copying and distributing updated machine learning models to each robot (which would consume significant bandwidth and energy), the patent copies only the lightweight embedding representations to the robots' local caches. These embeddings are compact data structures that can be rapidly transmitted and stored, enabling fleet-wide knowledge sharing with minimal energy expenditure compared to full model updates.
3Productivity
If robots share learned information frequently across the fleet, then knowledge dissemination speed increases, but the complexity of managing and synchronizing information across multiple robots increases
Solution Approach 1:
The patent uses embedding representations as simplified copies of learned information that can be easily transmitted and stored across the robot fleet. These embeddings serve as compact proxies for complex learned knowledge, allowing robots to share and access information efficiently without requiring complex synchronization protocols. The server maintains a centralized cache of embeddings that robots can query and update, providing a simple master-copy architecture that minimizes management complexity.
Solution Approach 2:
The patent introduces a server system as an intermediary that manages the embedding cache and handles communication between robots. This intermediary consolidates the complexity of information management, providing standardized interfaces for robots to upload and retrieve embeddings without requiring direct peer-to-peer coordination. The server mediates all knowledge sharing operations, simplifying the overall system architecture and reducing the computational burden on individual robots.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media for evaluating robot learning. In some implementations, one or more computers receive object classification examples from a plurality of robots. Each object classification example includes (i) an embedding that a robot generated using a machine learning model, and (ii) an object classification corresponding to the embedding. The object classification examples are evaluated based on a similarity of the received embeddings with respect to other embeddings. A subset of the object classification examples is selected based on the evaluation of the quality of the embeddings. The subset of the object classification examples is distributed to the robots in the plurality of robots.