Integrated Neural Skill Development for Robotics Simulation
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Solution Overview
Problem
The development of neural skills for robotics systems is time-consuming and requires expertise from both robotics and deep learning specialists, involving complex data gathering, neural network design, and tuning, making it burdensome and inefficient.
Innovation Solution
A computerized engineering tool that integrates a physics engine, neural data editor, experiment editor, neural skills editor, and machine learning environment, allowing direct interaction with a virtual world to simplify data generation and neural network design, reducing the need for manual data extraction and code development, and enabling a user-friendly interface for both robotics and machine learning experts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural skills are developed using traditional machine learning techniques, then the robotics system can learn complex skills, but the development process becomes time-consuming and requires multiple expert disciplines
Solution Approach 1:
The patent merges the physics engine and machine learning environment into a single integrated development platform. This combination allows simultaneous execution of physics simulations and neural network training, eliminating the need for separate data extraction processes and reducing development time while maintaining complex skill learning capabilities
Solution Approach 2:
The patent introduces a neural data editor as an intermediary component that automatically generates and manages training data. This mediator handles the complex data gathering and preprocessing tasks between the physics engine and neural network, reducing the expertise burden and accelerating development
2Measurement precision
If neural skills are developed with comprehensive data gathering and tuning, then the model accuracy improves, but the development complexity increases
Solution Approach 1:
The physics engine automatically generates training data and performance metrics without requiring manual data extraction or processing. The system self-services by providing ready-to-use training datasets and evaluation metrics directly within the integrated environment, maintaining high accuracy while reducing development complexity
Solution Approach 2:
The system performs preliminary data generation and preprocessing actions automatically before neural network training begins. By pre-generating comprehensive training datasets and configuring experiment parameters in advance through the neural data editor, the system ensures high model accuracy while simplifying the overall development process
3Manufacturing precision
If handcrafted skills are implemented by robotics experts, then the skills can be precisely controlled, but the development process becomes time-consuming
Solution Approach 1:
The patent replaces manual handcrafting processes with automated neural network-based skill generation. The machine learning environment automatically learns and generates control policies from simulated experiences, substituting the time-consuming manual programming process while maintaining precise skill control through the integrated physics engine
Data Source
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AI summary
Computerized engineering tool and methodology to develop neural skills for computerized autonomous systems, such as a robotics system (50), are provided. A disclosed computerized engineering tool (10) may involve an integrated arrangement of respective modular functionalities arranged in a closed loop, such as may include a physics engine (14), a neural data editor (16), an experiment editor (18), a neural skills editor (20), and a machine learning environment (22). Disclosed embodiments are conducive to cost-effectively simplifying development efforts involving neural skills, such as by reducing the time involved to develop the neural skills involved in any given robotics system and by reducing the level of expertise involved to develop neural skills.