Grasp Neural Network Output Transformation for Covariate Shift
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Solution Overview
Problem
Covariate shift in grasping neural networks, which occurs when the distribution of input data changes between training and inference regimes, leading to sub-optimal or incorrect labels for pixels, resulting in poor robotic grasping operations.
Innovation Solution
Transform the output of grasping neural networks to address covariate shift by filtering out incorrectly labeled grasp scores and adjusting grasp locations to avoid edges and potential collisions, without the need for retraining or regenerating datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If grasping neural networks are trained on synthetic data, then training efficiency and safety are improved, but covariate shift occurs when deployed with real camera data, degrading performance
Solution Approach 1:
The patent introduces an output transformation layer as an intermediary between the neural network and the grasp execution. This transformation layer adjusts the network outputs to compensate for covariate shift, effectively mediating between synthetic training data and real-world deployment conditions without requiring retraining
Solution Approach 2:
The patent modifies the neural network output parameters through transformation functions that adjust grasp scores and locations based on the detected covariate shift. This allows the system to adapt to real camera data distribution by changing the output parameters dynamically
2Measurement precision
If neural network output is transformed to address covariate shift, then grasp location accuracy is improved, but additional processing steps are required
Solution Approach 1:
The patent performs covariate shift compensation and output transformation as preliminary actions during the inference process. By addressing the covariate shift early in the pipeline through transformation layers, the system avoids more complex post-processing steps that would be required otherwise
Data Source
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AI summary
A covariate shift generally refers to the change of the distribution of the input data (e.g., noise distribution) between the training and inference regimes. Such covariate shifts can degrade the performance grasping neural networks, and thus robotic grasping operations. As described herein, an output of a grasp neural network can be transformed, so as to determine appropriate locations on a given object for a robot or autonomous machine to grasp.