Cloud Neural Network Training Using Robot Sensory Data
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Solution Overview
Problem
Current methods for training neural networks are costly and labor-intensive due to the need for annotating sensory data from robots, and there is a lack of efficient systems for utilizing sensory data collected by robots to enhance their functionality.
Innovation Solution
A method for training neural networks on a cloud server using sensory data from robots, where sensor data is received, labeled, and used to develop a model that can identify training features, which is then communicated to the robots, enabling them to perform tasks based on detected features and allowing for further training and model enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained using manually annotated sensory data from robots, then the model accuracy and feature identification capability are improved, but the training cost and labor requirements increase substantially
Solution Approach 1:
The system enables robots to perform self-training by collecting their own sensory data during normal operations and using it to train neural networks. The robots autonomously generate training datasets from their sensor inputs without requiring external human intervention for data collection, thereby reducing labor costs and training time while maintaining model accuracy.
Solution Approach 2:
The system collects and stores sensory data from robots during their normal operations in advance, building up a repository of training data before actual model training is needed. This preliminary data collection phase allows the training process to proceed efficiently without requiring real-time human annotation, reducing both time and labor costs.
2Reliability
If substantial numbers of training pairs are provided for neural network training, then the model learning quality is improved, but the data annotation cost and processing complexity increase
Solution Approach 1:
Robots automatically generate training pairs from their own sensory data during normal operations. The system leverages the robots' inherent sensing capabilities to create labeled training data without requiring external annotation processes, thereby improving model learning quality while reducing processing complexity and costs.
Solution Approach 2:
The system enables sensory data collected for robot operation to serve dual purposes: both guiding robot behavior and training neural networks. This multi-functionality allows the same data to improve both operational performance and model learning quality without requiring separate data collection and processing systems.
3Adaptability or versatility
If robots with varying computing power are used, then system adaptability and resource utilization are improved, but coordinating training across distributed robots increases system complexity
Solution Approach 1:
Each robot independently trains neural networks using its own sensory data and computing resources. The system eliminates the need for centralized coordination by enabling autonomous self-training at each robot, thereby maintaining adaptability across diverse hardware while reducing coordination complexity.
Solution Approach 2:
The training process is segmented and distributed across multiple independent robots rather than centralized. Each robot performs training locally using its own computational resources, allowing the system to adapt to varying computing powers without requiring complex inter-robot coordination mechanisms.
4Measurement precision
If sensory data is collected during robot operations, then data accuracy and reliability are improved, but bandwidth usage and communication overhead increase
Solution Approach 1:
Robots perform training locally using their own collected sensory data without requiring extensive data transmission to centralized servers. This self-service approach maintains data accuracy from actual robot operations while minimizing communication bandwidth usage by processing data at the source.
Solution Approach 2:
The system extracts only the essential training data and model parameters for transmission between robots and servers, rather than transmitting complete sensory datasets. This extraction approach preserves data accuracy while significantly reducing communication bandwidth requirements.
Data Source
AI summary
Systems and methods for training neural networks on a cloud server using sensory data collected by plurality of robots is disclosed herein. The model may be derived from one or more trained neural networks, the neural networks being trained using data collected by one or more robots. Advantageously, data collection by robots may enhance consistency, reliability, and quality of data received for use in training one or more neural networks. The model may be utilized by robots, upon sufficient training of the neural networks, such that the robots may identify features within their environments. Advantageously, the model may be trained on a cloud server and utilized by individual robots for use in enhancing autonomy of the robots, wherein the utilization of the model requires significantly fewer computational resources than training of the neural networks to develop the model.