Robotic Pick ML Retraining for Edge Cases From Incomplete Data
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Solution Overview
Problem
Traditional machine learning systems in automated industrial robotic picking systems struggle with edge cases, leading to poor performance and requiring human intervention, due to their inability to effectively handle incomplete and inaccurate training data.
Innovation Solution
The system generates updated ML models that account for edge cases by using human-in-the-loop data to train new models, which are then tested and deployed to improve pick performance without requiring extensive new training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ML models are trained with more training data to improve edge case performance, then the model accuracy on tail scenarios improves, but the time and cost to obtain training data increases significantly
Solution Approach 1:
The system implements a feedback loop where the ML model's performance is continuously monitored, and when edge cases are detected (low confidence predictions or failed picks), these cases are automatically collected and used to trigger model retraining. This closed-loop feedback mechanism ensures that training data is obtained efficiently by focusing only on the specific edge cases that need improvement, rather than collecting large volumes of general training data.
Solution Approach 2:
The system enables self-service by allowing the ML model to automatically identify its own weaknesses through confidence scoring and performance monitoring. When the model encounters edge cases, it autonomously flags these instances and initiates the retraining process without requiring external intervention or manual data collection, thus reducing the time and resources needed to improve edge case performance.
2Loss of time
If human intervention data is used to quickly obtain training data, then the time to update models decreases, but the data quality and completeness deteriorate
Solution Approach 1:
The system merges human intervention data with synthetic training data generated through data augmentation techniques. Human-provided pick points serve as seeds, and the system automatically generates additional training samples by applying transformations (rotations, translations, scaling) and synthesizing related edge cases. This combination preserves the speed advantage of human data collection while compensating for data quality limitations through automated augmentation.
Solution Approach 2:
The system performs preliminary data preparation by pre-processing and augmenting human intervention data before it is used for training. This includes automatically generating additional pick points around human-provided points, creating synthetic edge cases, and preparing the data in the correct format. This preliminary action ensures that even limited human data is maximally utilized and quality deficiencies are mitigated before the actual training process begins.
3Adaptability or versatility
If ML models focus on common scenarios to avoid overfitting, then the model generalization improves, but the performance on rare edge cases deteriorates
Solution Approach 1:
The system applies local quality by implementing different training strategies for different parts of the data distribution. Common scenarios are handled by the main ML model trained on general data to ensure good generalization, while edge cases are specifically addressed through targeted data augmentation and selective retraining. This allows the model to have different levels of specialization for different scenarios without compromising overall generalization.
Solution Approach 2:
The system applies partial action by focusing training efforts selectively on edge cases rather than attempting to improve all scenarios uniformly. When edge cases are detected, the system triggers targeted retraining specifically for those scenarios using augmented data, rather than retraining the entire model on all data. This partial focus on problematic areas improves edge case performance while maintaining the model's overall generalization capability.
Data Source
AI summary
The present invention relates to systems and methods for accounting for edge cases (i.e. tail data) in automated decision making systems, for example automated robotic picking systems. The systems and methods provide for retraining machine learning (ML) models so that the edge cases can be handled in a manner that requires less (or no) human intervention. The disclosed systems and methods create updated ML models, replacement ML models, and/or supplementary ML models that can provide better performance (e.g. improved automated robotic picking) when edge cases are encountered. Furthermore, the present inventions disclose systems and methods for obtaining training data faster and in a more cost effective manner, which enables the systems and methods disclosed herein to update models at a faster rate, thereby enabling broader, system-wide handling of edge cases in a more effective and efficient manner.