Robot Controller Configuration Using Simulation-Trained Agents
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Solution Overview
Problem
Existing methods for configuring robot controllers for predefined tasks are inefficient and require manual programming, which is time-consuming and lacks flexibility.
Innovation Solution
A method involving machine learning-based training of an agent using robot and environmental parameters in simulations, followed by configuring the robot's control system based on the trained agent, utilizing reinforcement learning and neural networks, with optional user input and cloud-based simulations to enhance efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual programming is used to configure robot controllers, then the configuration can be performed with simple tools, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual programming operations with machine learning-based automatic generation. The system uses trained ML models to automatically generate robot controller configurations from task descriptions and environmental models, substituting the mechanical process of manual coding with an automated intelligent system that significantly reduces configuration time and increases productivity
Solution Approach 2:
The system enables self-service configuration where the robot controller automatically generates its own control programs based on input parameters and trained machine learning models. The configuration process becomes self-directed, requiring minimal human intervention and eliminating the need for manual programming expertise, thereby dramatically reducing the time loss associated with traditional configuration methods
2Adaptability or versatility
If traditional configuration methods are used, then the system structure remains simple, but the system lacks flexibility and adaptability
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models with extensive robot parameters, environmental models, and task scenarios before actual deployment. This pre-computation phase creates adaptable configuration systems that can quickly respond to various tasks without requiring complex real-time processing, thereby increasing flexibility while managing system complexity through advance preparation
Solution Approach 2:
The system leverages parameter changes by utilizing diverse robot parameters (kinematic, dynamic, geometric) and environmental model parameters as inputs to the machine learning models. The trained models learn to generate appropriate controller configurations by processing these varying parameters, enabling flexible adaptation to different robot types, environments, and tasks without increasing inherent system complexity
3Manufacturing precision
If machine learning training is performed with comprehensive robot and environmental parameters, then task execution accuracy improves, but the training process becomes more complex and resource-intensive
Solution Approach 1:
The patent applies segmentation by dividing the comprehensive training process into distinct modules: robot parameter processing, environmental model processing, simulation environment creation, and model training. This modular approach allows each component to be developed and optimized independently, managing training process complexity while maintaining the ability to process comprehensive parameters for high task execution accuracy
Solution Approach 2:
The system introduces simulation environments as an intermediary between parameter input and model training. The simulation environment serves as a mediator that processes comprehensive robot and environmental parameters in a controlled virtual setting before deploying to real robots, thereby improving task execution accuracy while managing training complexity through layered processing
Data Source
Figure 1~3
AI summary
A method according to the invention for configuring a controller (2) of a robot (1) to perform a predetermined task comprises the steps: - Acquiring (S10, S20) at least one robot parameter and at least one environmental model parameter; - Training (S40) an agent using at least one simulation based on the acquired robot parameter and environmental model parameter by means of machine learning based on a predetermined cost function; and - Configuring (S50) the controller of the robot based on the trained agent.