Autonomous Device Learning via Randomized Effector Exploration
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Solution Overview
Problem
Existing artificial intelligence methods require extensive training data selection and preparation, which limits their adaptability and can introduce errors, and distributed learning systems face complexity and coordination challenges.
Innovation Solution
A method for autonomously controlling devices through randomized actuation of effectors, using internal and external sensors to log interrelationships between effector actions and environmental/internal properties, converting these into action instructions, and iteratively learning and refining these instructions to achieve predefined targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training data is selected and specified for artificial intelligence learning, then learning accuracy can be improved, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system performs self-learning by autonomously generating training data through randomized actuation of its own effectors and recording the outcomes via sensors. This eliminates the need for external human preparation of training data, making the system self-sufficient in creating its own learning corpus while maintaining accuracy.
Solution Approach 2:
The system performs preliminary randomized actuations of effectors to generate training data before actual task execution. This preliminary exploration phase creates the necessary training corpus in advance, allowing the system to learn without requiring time-consuming external data preparation during deployment.
2Measurement precision
If training data is selected and specified for artificial intelligence learning, then learning accuracy can be improved, but the adaptability is limited to the training data
Solution Approach 1:
The system continuously updates and refines its training data through ongoing randomized actuations and sensor recordings. This dynamic approach allows the training corpus to evolve and adapt to new situations and environments, preventing the system from being limited to static pre-defined training data while maintaining learning accuracy.
Solution Approach 2:
The system autonomously generates new training data when encountering novel situations, rather than relying on pre-collected datasets. This self-service capability ensures the system remains adaptable to new environments and tasks by creating relevant training examples on-demand.
3Productivity
If distributed learning is implemented across several devices, then problem-solving capability is improved, but control and coordination complexity increases
Solution Approach 1:
Multiple devices merge their sensor recordings and learned knowledge into a shared training corpus. By combining their individual learning experiences, the system achieves enhanced problem-solving capability while avoiding the coordination complexity of distributed learning through centralized knowledge integration.
4Extent of automation
If randomized actuation is used for initialization, then autonomous learning capability is improved, but energy consumption increases
Solution Approach 1:
The system performs randomized actuation partially - only during initialization and periodic updates rather than continuously. This partial application maintains autonomous learning capability while significantly reducing overall energy consumption compared to constant randomized exploration.
Solution Approach 2:
Randomized actuation is performed periodically rather than continuously, with intervals between exploration phases. This periodic approach sustains autonomous learning capability over time while managing energy consumption through structured rest periods between exploration cycles.
Data Source
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AI summary
The present invention relates to a method for the autonomous control of a device, wherein the device generally moves in a physical real world and no longer needs to be restricted with regard to its physical design. The method offers the advantage that the device performs autonomous learning and continuously improves the learned knowledge or behavior. In general, the disadvantage of the prior art, which requires the creation of training data as is the case with conventional artificial intelligence methods, is overcome. The method is generally universally applicable, and the device learns autonomously, constantly correcting its own knowledge base. Furthermore, a device configured to execute the method is proposed, as well as a system arrangement comprising several of the proposed devices.Furthermore, a computer program product and a computer-readable storage medium are proposed, which execute the process steps or cause a computer to execute the process.