Modular Medical Robot Configuration Using GAN-Based Generation
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Solution Overview
Problem
Conventional modular robotic systems struggle to generalize to complex morphologies for specific tasks due to limited module types and stochastic configuration processes, making it difficult to determine optimal parameters and arrangements for complex tasks like medical applications.
Innovation Solution
A machine-learned model, utilizing a generative adversarial network (GAN), is employed to automatically configure modular robotic systems by projecting task-specific specifications into optimal configurations, considering factors like force, motion, compliance, and interaction with anatomy, through a machine-learning system that includes a neural network to translate specifications into robotic configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual or iterative configuration by human designer is used, then design flexibility is maintained, but the complexity of determining optimal parameters and arrangements for complex tasks increases significantly
Solution Approach 1:
The patent replaces the manual iterative design process with a machine learning system that automatically generates optimal robotic configurations. The neural network takes task requirements as input and directly outputs configuration parameters, substituting human designer iterations with automated computational optimization that handles complex morphologies and module arrangements efficiently.
Solution Approach 2:
The system transforms the configuration problem from manual parameter tuning to automated parameter generation. The machine learning model learns optimal parameter settings during training and can rapidly generate configurations for new tasks by changing input specifications, avoiding the need for repeated manual parameter adjustments.
2Productivity
If stochastic process is followed to achieve desired configuration, then random exploration is performed, but the ability to generalize to complex morphologies for specific tasks fails
Solution Approach 1:
The system performs preliminary learning during a training phase where the neural network is exposed to many example configurations and their performance outcomes. This preliminary action builds learned knowledge that enables the system to generalize to new complex tasks without requiring stochastic exploration during actual configuration generation.
Solution Approach 2:
The machine learning system learns to copy successful configuration patterns from training examples. By memorizing and reproducing effective module arrangements and parameter settings from the training dataset, the system can generate appropriate configurations for new tasks that resemble the training examples, achieving generalization without stochastic search.
3Measurement precision
If brute force calculation is used to determine optimal parameters, then exhaustive search is performed, but the computational difficulty for complex modular components with multiple types and settable parameters becomes prohibitive
Solution Approach 1:
The system performs exhaustive optimization during the offline training phase, where the neural network learns from many example configurations and their optimal parameters. This preliminary calculation stores learned knowledge that enables rapid configuration generation during online use without requiring brute force search for each new task.
Solution Approach 2:
The patent replaces brute force calculation with a trained neural network that directly predicts optimal configurations. The computationally intensive optimization is substituted by the learned mapping from task requirements to configuration parameters, dramatically reducing calculation time while maintaining accuracy.
Data Source
AI summary
A generative adversarial network (GAN) (21, 24), or any other generative modeling technique, is used to learn (12) how to generate (68) an optimal robotic system given performance, operation, safety, or any other specifications. For instance, the specifications may be modeled (65) relative to anatomy to confirm satisfaction of anatomy-based or another task specific constraint. A machine-learning system, for instance neural network, is trained (12) to translate given specifications to a robotic configuration. The network may convert task-specific specifications into one or more configurations of robot modules into a robotic system. The user may enter (67) changes to performance in order for the network to estimate (62) appropriate configurations. The configurations may be converted (64) to estimated performance by another machine-learning system, for instance neural network, allowing modeling (65) of operation relative to the anatomy, such as anatomy based on medical imaging. The configuration satisfying the constraints from the modeling (65) may be assembled (69) and used.