Robot Controller Training With Stochastic Simulation Variations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for configuring robot controllers for specific tasks are inefficient, relying on manual programming and struggling to adapt to variations in robot and environmental parameters, leading to inflexible and less robust application execution.
Innovation Solution
A method involving stochastic parameter modeling and simulation-based machine learning to train control and classification agents, using randomized robot and environmental parameters to improve the robustness and flexibility of robot application execution, including multi-stage simulations and reinforcement learning for anomaly and error detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual programming is used to configure robot controllers, then the configuration process is straightforward and controllable, but the adaptability to parameter variations and robustness are poor
Solution Approach 1:
The system performs preliminary actions by training control agents and classification agents through simulations before actual robot operation. The training data is prepared in advance using simulated robot executions with varied parameters, enabling the robot to adapt to parameter variations without manual reprogramming during actual operation.
Solution Approach 2:
The system creates a virtual copy of the robot execution environment through simulations. These simulations replicate robot operations with varied parameters, allowing the training of agents without affecting the actual robot. The simulated environments serve as copies that capture the essential dynamics and variations.
2Reliability
If simulations with randomized parameters are used to train control agents, then the robustness and flexibility of application execution are improved, but the training time and computational resources increase
Solution Approach 1:
The system maintains continuous useful action during the training process by running multiple simulations in parallel and continuously improving the control agents through iterative learning. The training process is designed to run continuously with accumulating training data, converting what would be idle training time into productive improvement of system performance.
Solution Approach 2:
The system applies partial training approaches by focusing simulations on critical parameter variations and task-critical scenarios rather than exhaustively training all possible parameter combinations. This allows achieving sufficient robustness with reduced training time by concentrating computational resources on the most impactful training cases.
3Adaptability or versatility
If machine learning is used to train control agents, then the flexibility and adaptability of the robot controller are improved, but the complexity of the control system increases
Solution Approach 1:
The system introduces classification agents as intermediaries between the robot's sensors and control actions. These agents first classify the current situation by comparing sensor data with training patterns, then select appropriate control strategies. This intermediary layer manages complexity by breaking down complex decisions into classification and execution stages.
Solution Approach 2:
The control agents are designed as universal components that can handle multiple different tasks and parameter variations through a single trained model. Rather than creating separate controllers for each task or parameter set, the universal agent adapts to different scenarios based on its training, reducing overall system complexity while maintaining flexibility.
Data Source
AI summary
A method for carrying out an application using at least one robot includes, repeatedly ascertaining a stochastic value of at least one robot parameter and/or at least one environmental model parameter; and carrying out a simulation of the application on the basis of the ascertained stochastic value, training at least one control agent and/or at least one classification agent using the simulations by machine learning, and carrying out the application using the robot. The method may further include configuring a controller of the robot, by means of which the application is carried out wholly or in part, based on the trained control agent, and/or classifying the application using the trained classification agent.