Machine Learning Model Adaptation for Changed Robotic Gripping Conditions
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Solution Overview
Problem
Existing machine learning models for robotic gripping face performance degradation when conditions in the control situation change, such as changes in camera settings, objects, or environment, requiring extensive supervised training with annotated data.
Innovation Solution
A method for adapting a machine learning model to a changed control situation by detecting sensor data elements, generating multiple augmentations, obtaining outputs from a first instance of the model, combining outputs to determine target outputs, and adapting a second instance of the model to reduce a total loss, thereby enabling self-monitored test time adaptation without explicit annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If supervised learning with annotated training data is used to adapt the machine learning model to changed conditions, then gripping performance capability is improved, but the effort and time required for data generation and training increases significantly
Solution Approach 1:
The system performs self-adaptation by using its own outputs as target values for training. The machine learning model generates predictions that are then used as supervision signals to update itself, eliminating the need for external annotated training data while maintaining performance improvement in changed conditions
Solution Approach 2:
A loss function acts as an intermediary that bridges the model's predictions and the target values. The loss function quantifies the difference between predicted and target outputs, enabling gradient-based optimization without requiring manual annotations, thus reducing training time while maintaining reliability
2Reliability
If supervised learning with annotated training data is used to adapt the machine learning model to changed conditions, then gripping performance capability is improved, but the complexity of the adaptation process increases due to data generation and annotation requirements
Solution Approach 1:
The model adapts itself automatically by using its own predictions as supervision signals. This self-service mechanism eliminates the need for complex data generation, annotation, and curation processes, significantly reducing adaptation process complexity while maintaining performance improvement
Solution Approach 2:
The method extracts the essential training signal directly from the model's own outputs without requiring external annotated data. By taking out only the necessary information (prediction errors) and using it for self-updating, the complexity of the adaptation process is reduced while maintaining effectiveness
3Reliability
If the machine learning model is retrained with new training data to adapt to changed conditions, then the performance capability is improved, but additional annotated training data must be generated requiring great effort
Solution Approach 1:
The model performs self-adaptation by using its own predictions as target values for updating its parameters. This eliminates the need for manual data generation and annotation efforts, making the adaptation process easy to implement while maintaining performance capability improvement in changed conditions
Solution Approach 2:
The method changes the model's internal parameters (weights and biases) directly through gradient descent optimization using self-generated supervision signals. This parameter-based adaptation approach avoids the need for retraining with new annotated data, significantly easing the adaptation process while maintaining performance improvement
Data Source
AI summary
A method for adapting a machine learning model to a changed control situation. The method includes detecting sensor data elements in the changed control situation; for each ascertained sensor data element generating multiple augmentations of the sensor data element; generating, for each augmentation, a respective output by means of a first instance of the machine learning model; ascertaining a target output for the sensor data element by combining the generated outputs; and ascertaining a loss between an output of a second instance for the sensor data element and the ascertained target output; and adapting the second instance of the machine learning model in order to reduce a total loss, which contains the ascertained losses.