Imitation Learning Profiles for Quality-Yield Robot Switching
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Solution Overview
Problem
Current imitation learning techniques for robots fail to differentiate between different human workers' styles, resulting in process profiles that do not prioritize quality or yield effectively, lacking nuance and flexibility in manufacturing environments.
Innovation Solution
Implement diversified imitation learning by clustering human operators based on metadata such as outcome data, generating multiple process profiles that reflect different worker styles, allowing for flexible operation modes that prioritize quality or yield as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current imitation learning techniques are used for robots, then robots can learn from human operators, but they fail to differentiate between different worker styles, resulting in process profiles that do not prioritize quality or yield effectively
Solution Approach 1:
The patent segments the learning process by creating multiple process profiles from clustered sensor data, where each profile represents a different worker style. This segmentation allows the system to differentiate between workers who prioritize quality versus those who prioritize yield, resolving the contradiction by enabling style-specific learning rather than a single averaged profile
Solution Approach 2:
The patent applies local quality by generating specialized process profiles tailored to specific worker styles. Each profile contains localized optimizations for either quality or yield based on the individual worker's demonstrated preferences and performance patterns, allowing the robot to adapt its behavior to match the specific quality requirements of each worker style
2Adaptability or versatility
If current imitation learning techniques are used for robots, then robots can learn from human operators, but the system lacks nuance and flexibility in manufacturing environments
Solution Approach 1:
The patent segments sensor data into distinct clusters representing different worker styles, preserving the nuanced characteristics of each worker rather than averaging them. This segmentation maintains the information diversity needed for operational flexibility while preventing loss of worker style nuances
Solution Approach 2:
The patent changes parameters by creating multiple process profiles with different parameter settings that reflect various worker styles. Each profile contains adjusted parameters for quality prioritization, yield prioritization, and other operational characteristics, enabling the system to capture and utilize worker style nuances without information loss
3Adaptability or versatility
If multiple process profiles are generated for different worker styles, then operational flexibility is enhanced, but system complexity increases
Solution Approach 1:
The patent applies universality by designing a single robotic system that can perform multiple functions through different process profiles. The same robot hardware can dynamically switch between quality-prioritized mode, yield-prioritized mode, and other operational modes based on the selected profile, avoiding the need for multiple specialized robots while maintaining operational flexibility
Solution Approach 2:
The patent implements dynamics by enabling real-time switching between different process profiles based on operational requirements. The system can dynamically adjust its behavior by selecting appropriate profiles rather than being fixed to a single operational mode, managing complexity through flexible reconfiguration rather than hardware multiplication
Data Source
AI summary
Disclosed herein are embodiments of systems and methods for diversified imitation learning for automated machines. In an embodiment, a process-profiling system obtains sensor data captured by a plurality of sensors that are arranged to observe one or more human subjects performing one or more processes to accomplish one or more tasks. The process-profiling system clusters the sensor data based on a set of one or more process-performance criteria. The process-profiling system also performs, based on the clustered sensor data, one or both of generating and updating one or more process profiles in a plurality of process profiles. The process-profiling system selects, for one or more corresponding automated machines, one or more process profiles from among the plurality of process profiles, and the process-profiling system configures the one or more corresponding automated machines to operate according to the selected one or more process profiles.