Robotic Manipulation Event Validation Using HMM Classifiers
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Solution Overview
Problem
Modern inventory systems face challenges in efficiently managing large numbers of diverse inventory requests, leading to inefficient resource utilization, low throughput, long response times, and poor system performance.
Innovation Solution
The implementation of a classifier using hidden Markov models (HMMs) to validate robotic manipulation events in inventory systems, allowing for real-time or near-real-time validation and updating of robotic arm operations based on sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional inventory systems are used to manage large numbers of diverse inventory requests, then system capacity can be increased, but resource utilization becomes inefficient and response times increase
Solution Approach 1:
The patent replaces traditional mechanical inventory management systems with an autonomous robotic system that uses sensors, computer vision, and machine learning algorithms to automatically identify, sort, and manipulate inventory items. This substitution enables the system to handle diverse inventory requests more efficiently without proportionally increasing human labor or infrastructure requirements.
Solution Approach 2:
The robotic inventory management system operates autonomously to validate and execute manipulation events without continuous human intervention. The system self-manages task allocation, robot coordination, and event validation through embedded classifiers and machine learning models, reducing dependency on external control and improving response times.
2Adaptability or versatility
If traditional inventory systems are expanded to handle more tasks, then system capacity increases, but infrastructure changes become significant and complex
Solution Approach 1:
The patent employs universal robotic manipulators and end-effectors that can handle multiple types of inventory items through adaptive grasping and manipulation. The system uses standardized interfaces and modular components that can be reconfigured for different tasks, allowing the same infrastructure to serve diverse inventory management functions without requiring dedicated equipment for each task type.
Solution Approach 2:
The system utilizes dynamic task allocation and real-time planning algorithms that adapt to changing inventory requirements. The robotic controllers can dynamically adjust manipulation strategies, reassign tasks between robots, and modify operation parameters based on current system state and task priorities, enabling flexible scaling without rigid infrastructure changes.
3Productivity
If robotic manipulation events are not validated, then system throughput is maintained, but error rates increase and system reliability decreases
Solution Approach 1:
The patent implements a feedback-based validation system where sensors continuously monitor robotic manipulation events and feed data to classifiers that determine whether events were executed correctly. The system compares actual sensor readings against expected outcomes and triggers corrective actions or re-attempts when deviations are detected, ensuring high reliability without significantly impacting throughput.
Solution Approach 2:
The system performs preliminary validation by predicting expected sensor readings and manipulation outcomes before executing robotic actions. The classifier models anticipate the results of planned manipulation events and prepare validation criteria in advance, allowing for rapid post-execution verification and reducing the time penalty associated with validation.
Data Source
AI summary
Techniques for validating robotic manipulation events are described. In an example, a computer system controls an action of a robotic system. The action is associated with a manipulation of an item by the robotic system. The computer system receives time-series data of a sensor. The time-series data corresponds to the manipulation of the item by the robotic system. The computer system inputs the time-series data to a classifier trained for state-based event classifications. The computer system receives, from the classifier and based at least in part on the time-series data, an output that classifies the action in an event classification from the state-based event classifications. The computer system stores the event classification of the action.