Robotic Pick-Point Learning With Human-in-the-Loop AI Updates
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Solution Overview
Problem
Current industrial picking systems require significant human intervention due to limitations in handling edge cases and real-time learning, leading to inefficiencies and increased costs, as they struggle to adapt to non-stationary environments and dataset drift without overwhelming human operators.
Innovation Solution
A human-in-the-loop system utilizing multiple AI engines that enables real-time learning by allowing human operators to intervene and update AI models safely, reducing the risk of introducing errors or drift, and allowing for immediate deployment of updated models in the picking environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human operators intervene to handle edge cases and update AI models in real-time, then the system's adaptability to non-stationary environments improves, but the burden on human operators increases significantly
Solution Approach 1:
The patent introduces an intermediary system that automatically filters, prioritizes, and manages edge case requests before presenting them to human operators. This intermediary layer handles routine edge cases automatically and only presents complex cases requiring human judgment, thereby maintaining adaptability while reducing operator burden.
Solution Approach 2:
The system implements continuous feedback loops where human operator corrections and decisions are automatically incorporated into the AI model's learning process. This feedback mechanism enables the system to adapt to non-stationary environments by continuously learning from human expertise without requiring constant manual intervention.
2Reliability
If traditional teleoperation intervention systems are used to handle picking failures, then individual edge cases can be resolved, but the system becomes overwhelmed when multiple robots simultaneously request intervention
Solution Approach 1:
The patent segments the intervention system into multiple specialized AI handlers, each trained to handle specific types of edge cases. This segmentation allows different robots requesting intervention to be handled by different specialized handlers simultaneously, preventing system overload and maintaining throughput while reliably resolving diverse edge cases.
3Speed
If AI models are updated frequently to adapt to changing conditions, then the system's responsiveness to new data improves, but the risk of introducing errors and drift increases
Solution Approach 1:
The patent implements preliminary validation and testing procedures that automatically evaluate proposed model updates before deployment. This preliminary action ensures that updates improving learning speed do not compromise model accuracy, as potentially harmful updates are filtered out before they can affect production systems.
Solution Approach 2:
The system maintains backup models and implements rollback mechanisms that cushion against potential errors in updated models. This beforehand cushioning allows the system to experiment with faster learning updates while protecting production reliability through automatic fallback to proven stable models if issues arise.
Data Source
AI summary
In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may maintain an first dataset configured to select pick points for objects. The apparatus may receive, from a user device, a user dataset including a user selected pick point associated with at least one object and a first image of the at least one first object. The apparatus may generate a second dataset based at least in part on the first dataset and the user dataset. The apparatus may receive a second image of a second object. The apparatus may select a pick point for the second object using the second dataset and the second image of the second object. The apparatus may send information associated with the pick point selected for the second object to a robotics device for picking up the second object.