Robot Control Model Transfer Learning for Faster Teach-Through
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Solution Overview
Problem
Training artificial intelligence models to control complex mechanical systems, such as robots, is time-consuming and challenging due to the need for extensive individual training for each system, even when systems are similar, as minor variations in sensors, actuators, and environments prevent direct portability of trained models.
Innovation Solution
Implementing transfer learning by obtaining parameters from trained robot-control models and using them to initialize and adjust untrained models, reducing the need for extensive training through techniques like reinforcement learning and simulated annealing, allowing for faster convergence to optimal parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual training is performed for each robot system, then the model achieves high accuracy for that specific system, but the training time becomes extraordinarily long
Solution Approach 1:
The patent applies preliminary action by training a general robot control model in advance that can be quickly adapted to individual robot systems. Instead of training from scratch for each robot, the pre-trained model serves as a foundation that requires only minor adjustments and fine-tuning for each specific system, dramatically reducing training time while maintaining accuracy.
Solution Approach 2:
The patent utilizes parameter changes by adjusting model parameters through transfer learning and fine-tuning processes. The pre-trained model's parameters are modified adaptively for each individual robot system based on its specific characteristics, allowing the model to achieve high accuracy for each system without requiring complete retraining.
2Productivity
If transfer learning is used to reduce training time, then training speed improves, but the model may lose precision for individual robot variations
Solution Approach 1:
The patent applies local quality by making the model adaptable to local variations in individual robot systems. Through fine-tuning and system-specific adjustments, the model maintains its general knowledge from transfer learning while adapting to the specific characteristics of each robot, ensuring both speed and precision.
Solution Approach 2:
The patent implements dynamics by creating a flexible adaptation process that allows the model to dynamically adjust to individual robot systems. The fine-tuning mechanism enables the model to be flexible and responsive to system-specific variations, maintaining precision while benefiting from the speed of transfer learning.
3Reliability
If extensive training data is collected for each robot, then model robustness improves, but the complexity of the training process increases
Solution Approach 1:
The patent applies merging by combining training data and models across multiple robot systems. Instead of collecting extensive data for each individual robot, the system merges information from multiple sources through the pre-trained model, achieving robustness through aggregated knowledge while simplifying the training process for individual systems.
Data Source
AI summary
Disclosed techniques for decreasing teach times of robot systems may obtain a first set of parameters of a first trained robot-control model of a first robot trained to perform a task and determine, based on the first set of parameters, a second set of parameters of a second robot-control model of a second robot before the second robot is trained to perform the task. In some cases, a plurality of sets of parameters from trained robot-control models of respective robots trained to perform a task may be obtained. Thus, for example, a convergence of values of those parameters on a value, or range of potential values, may be determined. Embodiments may determine values for parameters of the control model of the (e.g., second) robot to be trained within a range, or a threshold, based on values of corresponding parameters of the trained robot(s).