System for continuous learning in AI-controlled autonomous devices

Abstract

System (100) for continuous learning in AI-controlled autonomous devices, comprising: a data acquisition module (101) configured to continuously collect real-time data from various sensors integrated into the autonomous device a learning machine (102) that uses reinforcement learning to process the collected data and refine decision strategies based on feedback received from the environment; a self-monitored learning component (103) that is able to identify patterns in unlabeled data to improve the learning process without relying on extensive labeled datasets; a model optimization unit (104) configured to dynamically adjust the parameters of the AI ​​model based on performance metrics derived from the learning machine; a federated learning coordinator (105) that enables collaborative learning between multiple autonomous devices, so that each device can share insights and knowledge while maintaining data privacy; a deployment and monitoring module (106) that ensures the seamless integration of updated AI models into the operating framework of the autonomous device and the monitoring of performance metrics in real time.

Description

Field of the invention:

[0001] The present invention relates to the field of artificial intelligence (AI) and autonomous devices. More specifically, it concerns a system for continuous learning in AI-driven autonomous devices that enables real-time adaptation and optimization of decision-making processes in dynamic environments. Background of the invention:

[0002] Significant progress has been made in the field of autonomous devices in recent years, particularly through the integration of artificial intelligence (AI) and machine learning (ML) technologies. These innovations have enabled devices to perform complex tasks such as navigation, object recognition, and decision-making with increasing precision. Despite these advances, however, existing AI-powered autonomous systems often rely on static machine learning models that are trained once and then deployed, without the ability to learn or adapt further after deployment.

[0003] One of the key limitations of these traditional models is their inability to handle new or unforeseen scenarios that deviate from the training data. Autonomous devices typically operate in dynamic environments where conditions can change rapidly and unexpectedly. For example, a self-driving vehicle may encounter new obstacles, altered road conditions, or unforeseen traffic patterns it has never seen before. Similarly, a robotic system in a manufacturing facility may need to perform new tasks or adapt to changing production requirements.

[0004] In such environments, static models can quickly become outdated, leading to suboptimal decisions and performance degradation. Autonomous systems either cannot respond appropriately to new conditions or require manual intervention, retraining, and redeployment. This limitation hinders the true potential of AI-powered autonomous devices, especially in mission-critical applications such as disaster relief, autonomous navigation in rough terrain, and industrial automation, where adaptability and continuous improvement are paramount.

[0005] Another challenge associated with traditional AI models is the need for large, labeled datasets to initially train them. Collecting and labeling large amounts of data is a resource-intensive process that can be impractical in certain domains. Furthermore, once the AI ​​model is deployed, it can be difficult to continuously update or improve it based on new experiences or new data types, especially when labeled data is scarce or unavailable.

[0006] There are efforts to address these problems through periodic retraining or by incorporating new datasets into the model. However, such approaches often require interrupting the autonomous system's operation, resulting in downtime and inefficiency. This is particularly problematic for systems where uninterrupted operation is critical, such as healthcare devices, defense applications, or large-scale logistics operations.

[0007] Furthermore, the emergence of distributed and collaborative AI systems, where multiple autonomous devices work in tandem or across different geographic regions, introduces another layer of complexity. Each device may have unique experiences or face different challenges, making centralized learning and manual model updates even less practical. In such contexts, it is critical that each autonomous device not only learns from its own experiences but also shares relevant knowledge with other devices so that the entire system can evolve and improve together.

[0008] To overcome these limitations, there is a growing need for autonomous devices with continuous learning capabilities. Continuous learning, also known as online or lifelong learning, enables AI models to adapt in real time by processing new information as it becomes available. This ability to learn from new data, refine decision-making strategies, and adapt to dynamic environments without halting operations or requiring manual intervention is critical for the next generation of intelligent autonomous systems.

[0009] Various research initiatives have proposed different methods for continuous learning, including reinforcement learning, self-supervised learning, and federated learning. These approaches aim to improve the adaptability of AI systems by enabling models to update themselves based on real-time interactions and experiences. However, integrating these methods into autonomous devices presents various technical challenges. These include ensuring efficient resource utilization, preserving data privacy, and avoiding model drift or catastrophic forgetting, in which newly learned information overwrites or conflicts with previous knowledge.

[0010] Given these challenges, the present invention proposes a comprehensive solution for continuous learning in AI-driven autonomous devices. By leveraging a combination of real-time data acquisition, reinforcement learning, self-supervised learning, and federated learning, the system enables autonomous devices to continuously improve their performance, adapt to new environments, and collaborate with other devices to enhance collective learning without compromising operational efficiency.

[0011] This invention represents a significant advance in the development of intelligent, adaptable, and resilient autonomous systems. Summary of the invention:

[0012] The present invention relates to a system for continuous learning in AI-controlled autonomous devices that enables these systems to adapt and optimize their performance in real time, without requiring manual intervention or downtime. This innovative solution addresses the limitations of conventional AI models, which often rely on static training and are unable to adapt to dynamic environments or new situations.

[0013] The proposed system is built around a modular framework that integrates several key components: 1. Data acquisition module: This module continuously collects data from various onboard sensors, allowing the system to monitor its environment and gather information necessary for learning and decision making. 2. Learning module: Using advanced machine learning techniques, including reinforcement learning and transfer learning, the learning engine processes real-time data to adapt and optimize AI models based on experience and feedback from the environment. 3. Federated Learning Coordinator: This component facilitates distributed learning among multiple autonomous devices, allowing them to collaboratively improve their models without compromising privacy and security. Each device can share insights and knowledge, accelerating the overall learning process. 4. Model optimization unit: Continuously refines AI models based on incoming data, improves decision-making strategies, and ensures that the system remains effective even under changing conditions. 5. Self-supervised learning component: This feature allows the system to learn from unlabeled or partially labeled data, thus expanding its ability to adapt to new scenarios without relying exclusively on large labeled datasets. 6. Deployment and Monitoring Module: This module ensures the seamless integration of updated AI models into the autonomous device's operational framework and monitors performance metrics to ensure optimal efficiency.

[0014] By integrating these components, the system enables autonomous devices to continuously learn, allowing them to remain effective in a variety of environments. The invention significantly improves the adaptability, resilience, and scalability of AI-powered autonomous systems, paving the way for their application in critical areas such as healthcare, disaster relief, industrial automation, and logistics. Overall, this system represents a transformative approach to improving the intelligence and operational capabilities of autonomous devices in an ever-evolving landscape. Short description of the drawing:

[0015] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. Fig. shows a block diagram illustrating the components of the continuous learning system (100) for AI-assisted autonomous devices. Detailed description of the invention:

[0016] At the heart of the invention is a sophisticated system for continuous learning in AI-controlled autonomous devices, enabling them to adapt and optimize their decision-making capabilities in real time. The fundamental premise of this system is to overcome the limitations of conventional AI models, which are often based on static datasets and are unable to cope with the dynamic nature of real-world environments.

[0017] The heart of this system is the data acquisition module, a crucial component responsible for continuously collecting a wealth of information from various sensors integrated into the autonomous device. These sensors can include cameras, LIDAR, GPS, and environmental sensors, among others. By collecting real-time data about the environment, the system creates a rich information base essential for subsequent learning and decision-making processes.

[0018] This data stream is processed by the learning engine, which forms the core of the system. The learning engine uses advanced machine learning techniques, specifically reinforcement learning and transfer learning. Reinforcement learning allows the device to learn from its actions by receiving feedback on the outcomes of its decisions. This feedback mechanism enables the autonomous device to continuously refine its strategies and improve its performance over time. Transfer learning further enhances this capability by allowing the knowledge acquired in a specific context to be applied to other, yet related, scenarios. For example, if a self-driving vehicle learns to navigate a particular city, it can transfer this knowledge to adapt more quickly when it enters a new one.

[0019] A central aspect of this invention is the inclusion of a federated learning coordinator, which enables multiple autonomous devices to participate in distributed learning processes. Each device can update its learning models based on its unique experiences while maintaining data privacy. This collaborative learning approach ensures that all devices benefit from the collective knowledge of the network, significantly increasing their overall performance. The federated learning mechanism is particularly advantageous in environments where data sharing is restricted or where privacy concerns exist, as it allows devices to learn and improve without exposing sensitive information.

[0020] These components are complemented by the model optimization unit, which plays a crucial role in continuously refining the AI ​​models based on the received real-time data. This unit uses sophisticated optimization algorithms such as stochastic gradient descent and Bayesian optimization to dynamically adjust the model parameters. In this way, the models become increasingly accurate and efficient, allowing the autonomous device to make informed decisions quickly and efficiently.

[0021] Another innovative feature of this system is the self-supervised learning component. This component enables the system to learn from unlabeled or partially labeled datasets. This addresses one of the biggest challenges of traditional machine learning, which relies heavily on large-scale labeled data for training. By using self-supervised learning techniques, the autonomous device can derive insights from its experiences and observations, further improving its adaptability and intelligence.

[0022] Finally, the deployment and monitoring module ensures that updated AI models are seamlessly integrated into the autonomous device's operating framework. This module continuously monitors key performance metrics, such as decision accuracy, processing latency, and resource consumption. This ensures that the system operates at maximum efficiency while enabling ongoing improvements based on real-world performance.

[0023] In summary, this invention represents a holistic approach to continuous learning in AI-powered autonomous devices. By combining real-time data acquisition, advanced learning techniques, collaborative learning, and continuous model optimization, the system enables autonomous devices to thrive in dynamic environments. This innovation not only improves the adaptability and resilience of these systems but also opens new avenues for their application in various sectors, including healthcare, logistics, disaster relief, and industrial automation. Ultimately, the continuous learning capabilities of this system represent a significant advance in the search for intelligent, autonomous solutions that can operate effectively in a constantly evolving environment.

[0024] The operational framework of the continuously learning system for AI-driven autonomous devices begins with the seamless integration of multiple components designed to work together, thereby improving the device's adaptability and performance in real-time environments.

[0025] Initially, the system initiates the data acquisition module, which continuously collects data from a variety of onboard sensors. These sensors, which may include cameras, LIDAR, ultrasonic sensors, and environmental detectors, serve as the device's eyes and ears, allowing it to perceive its surroundings in great detail. This real-time data acquisition is crucial to the learning process, as it lays the foundation for the experiences from which the device will learn and adapt.

[0026] Once the data is collected, it is transmitted to the learning engine. This component uses sophisticated machine learning algorithms, specifically reinforcement learning, to process the incoming information. The learning engine is designed to simulate a trial-and-error learning environment. As the autonomous device interacts with its environment, it receives feedback in the form of rewards or punishments based on its actions. In the case of a self-driving car, for example, successfully navigating traffic would result in a reward, while making a wrong turn would result in a penalty. This feedback loop allows the learning engine to evaluate the effectiveness of its actions and gradually refine its decision-making strategies based on the experience gained.

[0027] In parallel, the self-directed learning component works to enhance the device's learning capability. It uses the unlabeled data collected during operation to independently detect patterns and relationships. By identifying similarities in data points, the device can gain insights without relying exclusively on labeled datasets, which are often scarce and resource-intensive to acquire. This capability allows the device to adapt to new scenarios that it was not exposed to during initial training.

[0028] As the device learns from its environment, the Model Optimization Unit continuously refines the AI ​​model based on the performance metrics derived from the learning engine. It dynamically adjusts the model parameters to ensure that the decisions made by the autonomous device are as accurate and efficient as possible. This iterative optimization process enables the system to respond quickly to changes in its environment, thus increasing its operational effectiveness.

[0029] To further enhance the learning process, the system includes a federated learning coordinator. This component enables multiple autonomous devices to collaborate and share insights without compromising data privacy. Each device can learn from its own experiences while benefiting from the collective knowledge of the other devices in the network. For example, as a fleet of delivery drones encounters different obstacles or navigates diverse environments, they can share valuable insights, allowing each device to adapt more quickly to new challenges. This collaborative approach not only accelerates the learning process but also creates a robust network of intelligent devices capable of working together.

[0030] The Deployment and Monitoring module plays a critical role in ensuring that updated AI models are seamlessly integrated into the autonomous device's operating framework. Because new models are generated through the continuous learning process, this module ensures that the device can utilize the most up-to-date and effective models without interruption. It also monitors key performance indicators such as accuracy, processing speed, and resource utilization, allowing for real-time adjustments and ensuring optimal performance.

[0031] Essentially, the invention functions like a self-improving ecosystem in which the autonomous device learns, adapts, and optimizes its performance in real time. By effectively leveraging continuous learning, the system enables AI-driven autonomous devices to operate reliably in dynamic environments, overcome challenges, and improve their capabilities over time. This capability not only leads to improved efficiency and effectiveness in various applications but also lays the foundation for the future of intelligent autonomous systems that can continuously evolve in an ever-changing world. LIST OF REFERENCE SYMBOLS 100 System for continuous learning in AI-controlled autonomous devices 101 Data Collection Module 101a Combination of cameras 101b LIDAR 101c Ultrasonic Sensors 101d Environmental detectors 102 Learning Module 103 Component for self-supervised learning 104 Model Optimization Unit 105 Federated Learning Coordinator 106 Operation and Monitoring Module

Claims

[1] System (100) for continuous learning in AI-controlled autonomous devices, comprising: a data acquisition module (101) configured to continuously collect real-time data from various sensors integrated into the autonomous device a learning machine (102) that uses reinforcement learning to process the collected data and refine decision strategies based on feedback received from the environment; a self-supervised learning component (103) capable of identifying patterns in unlabeled data to improve the learning process without relying on large labeled data sets; a model optimization unit (104) configured to dynamically adjust the parameters of the AI ​​model based on performance metrics derived from the learning machine; a federated learning coordinator (105) that enables collaborative learning between multiple autonomous devices so that each device can share insights and knowledge while maintaining data privacy; a deployment and monitoring module (106) that ensures the seamless integration of updated AI models into the autonomous device's operational framework and the monitoring of performance metrics in real time. [2] The system of claim 1, wherein the data acquisition module (101) comprises a combination of cameras (101a), LIDAR (101b), ultrasonic sensors (101c), and environmental detectors (101d) to enhance the sensing capabilities of the device. [3] The system of claim 1, wherein the learning engine (102) uses transfer learning to apply knowledge gained from an operational context to different but related scenarios, thereby improving adaptability in different environments. [4] The system of claim 1, wherein the self-supervised learning component (103) uses clustering algorithms to automatically categorize incoming data and extract meaningful features, thereby improving the ability of the device to learn from new experiences. [5] The system of claim 1, wherein the model optimization unit (104) uses stochastic gradient descent and Bayesian optimization algorithms to refine the parameters of the AI ​​model based on real-time performance feedback. [6] The system of claim 1, wherein the federated learning coordinator (105) aggregates knowledge from multiple autonomous devices operating in different environments to improve the overall performance of the learning machine (102) without compromising data protection.

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