Robotics Neural Skill Development Using Closed-Loop 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 machine learning professionals, involving complex data gathering, neural network design, and tuning, which complicates the process and increases the time required to develop neural skills.
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 extensive coding and expertise, and incorporating a meta-learning optimizer to optimize task development based on previous experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural skills are developed using traditional machine learning techniques, then the system can learn complex patterns, but the development time and expertise requirements increase significantly
Solution Approach 1:
The patent uses simulation environments to create virtual copies of physical robot systems. Neural skills are trained in the simulated environment where data can be generated and experiments run without physical constraints. The trained neural networks are then transferred to the physical system, eliminating the need for time-consuming physical trial-and-error during development.
Solution Approach 2:
The system performs preliminary data generation and neural network training in a simulated environment before deployment to the physical robot. This preliminary action in virtual space prepares the neural skills in advance, so that when deployed physically, the system already possesses pre-trained capabilities that can be quickly adapted rather than trained from scratch.
2Measurement precision
If neural skills are developed with extensive data gathering and network tuning, then model accuracy improves, but the complexity of the development process increases
Solution Approach 1:
The simulation environment automatically generates training data by running virtual experiments with the robot performing various tasks. The system self-services by creating its own training dataset without requiring manual data collection or extensive configuration. The neural network architecture and training parameters are also automatically configured based on the task requirements, reducing development complexity.
Solution Approach 2:
The simulation environment serves multiple functions simultaneously: it generates training data, validates neural network architectures, tests robot performance, and prepares deployment configurations. This multi-functional platform consolidates what would otherwise be separate complex development tools into a single unified system.
3Reliability
If expert involvement is increased for neural network design and tuning, then skill quality improves, but the ease of operation decreases
Solution Approach 1:
The patent replaces manual expert tuning and configuration with automated algorithms. The simulation environment automatically configures neural network architectures, selects appropriate training parameters, and optimizes performance through self-contained training loops. This substitution of automated computational processes for manual expert adjustment maintains quality while dramatically improving ease of operation.
4Ease of manufacture
If handcrafted skills are used instead of neural skills, then development is simpler, but adaptability to new tasks is reduced
Solution Approach 1:
The system merges the simplicity of handcrafted skill definitions with the adaptability of neural networks. High-level task specifications can be defined in simple terms (similar to handcrafted approaches), while the simulation environment automatically generates the complex training data and neural network configurations needed for adaptability. This combination allows users to define tasks simply while the system handles the complex adaptation automatically.
Data Source
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.