Robot Teach-Through Initialization Using Transferred Control Parameters
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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, and existing approaches require significant training data and human supervision, leading to lengthy 'robot teach periods'.
Innovation Solution
The use of transfer learning, where parameters from pre-trained robot-control models are obtained and adjusted to expedite training on new tasks or environments, reducing the need for extensive training data and human supervision by initializing models with parameters from similar systems and adjusting them based on performance, thereby reducing teach times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual training is performed for each robot system, then the model achieves system-specific accuracy, but the training time becomes extraordinarily long
Solution Approach 1:
The patent applies preliminary action by pre-training a base model on a source robot system before deploying it to a target robot system. The base model learns task-relevant features and control strategies in advance, which are then transferred to the target system through parameter copying and fine-tuning, significantly reducing the training time required for the target system while maintaining task-specific accuracy
Solution Approach 2:
The patent utilizes parameter changes by copying model parameters (weights and biases) from the source system to the target system, then adjusting only the necessary parameters during fine-tuning. This selective parameter transfer and modification approach allows the model to adapt to the target system's specific characteristics without requiring complete retraining, thus resolving the contradiction between accuracy and training time
2Adaptability or versatility
If complete retraining is performed for each new robot system, then the model adapts perfectly to the new system, but extensive training data and human supervision are required
Solution Approach 1:
The patent applies copying by transferring the trained base model parameters from the source robot system to the target robot system. Instead of generating extensive training data for each new system, the patent copies the learned representations and control policies, requiring only minimal additional data for fine-tuning the copied model to the target system's specific characteristics
Solution Approach 2:
The patent achieves universality by creating a base model that can be transferred across multiple different robot systems. The base model learns general task-relevant features that are applicable to various systems, allowing the same pre-trained model to serve multiple functions across different robot platforms with minimal system-specific adaptation
3Ease of operation
If trial-and-error training is used to reach desired states, then the model learns task completion, but the process becomes extraordinarily tedious and time-consuming
Solution Approach 1:
The patent applies preliminary action by pre-training the base model to learn task completion strategies before deployment. The base model undergoes extensive trial-and-error training in advance to master the task, so that when deployed to new systems, it already possesses the learned knowledge and requires minimal additional trial-and-error tuning, dramatically reducing the teach period
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).