Robot Task-Batch Control Using Pretrained Parameter Set Switching
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Solution Overview
Problem
Existing robotic systems struggle to maintain optimal control when handling multiple task sets with different policy optimal strategies, especially in dynamic and varied industrial environments.
Innovation Solution
A flexible robot control method that stores multiple parameter sets of control strategies and switches among them based on task classification and priority, allowing for quick adaptation to unknown tasks and layouts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI-based control with reinforcement learning is used to adapt flexibly to various situations, then adaptability is improved, but training time increases making it hard to quickly establish optimal strategies
Solution Approach 1:
The system performs preliminary action by pre-training multiple AI models with different hyperparameter sets before actual operation. These pre-trained models are stored and can be quickly selected based on task requirements, eliminating the need for time-consuming training during actual operations. The controller pre-generates a plurality of AI models with different hyperparameters in advance, so when a new task arrives, the system can immediately select and use an appropriate pre-trained model rather than training from scratch.
Solution Approach 2:
The system applies parameter changes by varying hyperparameters (such as learning rate, batch size, network architecture parameters) to create multiple AI models with different characteristics. By changing these parameters during pre-training, the system generates a diverse set of models that can handle different task types and conditions. The controller selects the most appropriate hyperparameter configuration based on the specific task requirements, enabling quick adaptation without retraining.
2Productivity
If multiple task sets with different policy optimal strategies are operated in parallel, then productivity is improved, but maintaining optimal control becomes difficult
Solution Approach 1:
The system achieves universality by creating a multi-functional AI model selection mechanism that can handle multiple different task types with a single unified framework. The controller is designed to manage various task sets (order picking, inventory management, shipping) using the same underlying AI model architecture, selecting appropriate pre-trained models based on task classification. This universal controller maintains optimal control across diverse parallel operations without requiring separate dedicated systems for each task type.
Solution Approach 2:
The system maintains optimal control across parallel task sets by dynamically changing hyperparameter selections based on task characteristics. Each task type has associated optimal hyperparameter configurations that were determined during pre-training. When multiple task sets operate in parallel, the controller identifies the appropriate hyperparameter set for each task and loads the corresponding pre-trained AI model, ensuring each task receives optimal control parameters while maintaining system-wide productivity.
3Ease of operation
If rule-based control logic is used for automated operations, then ease of operation is improved, but flexibility to handle dynamic environments and varied orders deteriorates
Solution Approach 1:
The system replaces the mechanical rule-based control logic with an AI-based control system that uses neural networks and machine learning algorithms. Instead of relying on predefined if-then rules that require manual programming for each scenario, the AI model automatically learns optimal control strategies through pre-training and adapts to new situations. This substitution maintains ease of operation through automated decision-making while dramatically improving flexibility to handle dynamic environments and varied order types.
Solution Approach 2:
The system performs preliminary learning action during the pre-training phase, where the AI model learns optimal control strategies for various task types before actual operation. This preliminary action eliminates the need for manual rule creation and enables the system to handle dynamic environments from the start. The controller selects from pre-trained models that have already learned appropriate behaviors, providing both ease of operation and adaptability without requiring complex rule-based programming.
Data Source
AI summary
Systems and methods described herein are directed to an environment involving a plurality of robots, wherein for receipt of a plurality of orders, the systems and methods generate a plurality of task batches to fulfill the plurality of orders; generate a parameter set for execution by the plurality of robots to execute the plurality of task batches. For a determination by a controller that one or more of the plurality of robots is to execute the plurality of task batches, the systems and methods load the parameter set; and control the one or more of the plurality of robots based on the loaded parameter set to execute the task batch.