Reinforcement Learning for Automated Object Placement Optimization
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Solution Overview
Problem
The existing design process for mass-producing products is inefficient due to manual placement of target objects around specific objects, leading to increased work time, reduced efficiency, and inconsistent results among workers with different know-how.
Innovation Solution
A reinforcement learning apparatus and method that configures a learning environment based on user design data to optimize the position of target objects through simulation, using a simulation engine and reinforcement learning agent to determine optimal placement and provide feedback for decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual placement of target objects is used by workers, then design flexibility is maintained, but work time increases and productivity decreases
Solution Approach 1:
The patent replaces the manual mechanical design process with an automated reinforcement learning system. The RL agent automatically determines optimal positions of target objects around specific objects, substituting human workers' manual placement operations with an intelligent algorithm that processes design data and generates optimized layouts without manual intervention.
Solution Approach 2:
The reinforcement learning agent performs self-learning through trial and error in a simulated environment, automatically optimizing object placements without requiring continuous human guidance. The system captures workers' know-how and applies it autonomously to generate optimized designs, enabling the system to serve itself in the design optimization process.
2Manufacturing precision
If manual design process is used, then workers can apply their know-how, but results are inconsistent across different workers
Solution Approach 1:
The patent transforms the variable human factor (workers' different know-how) into a stable algorithmic parameter. By encoding design expertise into the reinforcement learning agent's reward function and state space, the system maintains consistent decision-making criteria across all design tasks, eliminating the variability inherent in manual processes while preserving the essence of expert knowledge.
Solution Approach 2:
The system creates a virtual copy of the design environment and objects, allowing the reinforcement learning agent to learn and optimize placements in simulation before applying results to actual design. This copying approach enables consistent application of learned optimization strategies across different design scenarios without being affected by individual worker variations.
3Productivity
If reinforcement learning is implemented, then work efficiency improves, but device complexity increases
Solution Approach 1:
The patent introduces a simulation environment as an intermediary between the reinforcement learning agent and the actual design process. The simulation engine creates virtual representations of design objects and constraints, allowing the RL agent to learn and optimize placements without directly interacting with complex real-world design systems. This intermediary layer simplifies the learning process while maintaining relevance to actual design scenarios.
Data Source
AI summary
Disclosed are a reinforcement learning apparatus and a reinforcement learning method for optimizing the position of an object based on design data. The present disclosure may configure a learning environment based on design data of a user and generate the optimal position of a target object, installed around a specific object during a design or manufacturing process, through reinforcement learning using simulation.