GPT Robotics OS Configuration for Secure Autonomous Deployment
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Solution Overview
Problem
Current technologies for mobile autonomous robotics lack the ability to rapidly specify and deploy robust and secure intelligence and behaviors across various environments, making them vulnerable to unsafe failure modes and malicious attacks.
Innovation Solution
A general-purpose robotics operating system (GPROS) combined with a generative pre-trained transformer (GPT) model enables the rapid generation and deployment of configuration data and service extensions for robotics platforms, allowing for extensive intelligence and behavior specification, ensuring robustness and security through a safety and security watchdog service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional robotics systems are used, then system simplicity is maintained, but the ability to rapidly specify and deploy robust intelligence and behaviors is limited
Solution Approach 1:
The patent applies preliminary action by pre-training the transformer model on extensive robotics-related data before deployment. The GPT model is trained in advance on configuration files, service definitions, and robotics knowledge, enabling it to rapidly generate accurate configurations and behaviors when deployed without requiring retraining for each specific task.
Solution Approach 2:
The patent introduces a transformer-based GPT model as an intermediary between the desired robot behavior and the actual system configuration. This intermediary translates natural language or high-level specifications into detailed configuration files and service definitions, bridging the gap between simple user input and complex system implementation.
2Reliability
If extensive intelligence and behaviors are incorporated into robots, then robustness and reliability improve, but system complexity increases
Solution Approach 1:
The patent segments the complex robot system into modular components including sensors, actuators, services, and configuration files. Each component is independently defined and can be selectively activated through configuration, allowing extensive intelligence to be incorporated while maintaining manageability through modular organization.
Solution Approach 2:
The patent uses parameter changes by dynamically configuring robot behaviors and capabilities through configuration files rather than hardcoding. The system can adjust parameters such as sensor thresholds, actuator limits, and service priorities through software configuration, enabling robustness without increasing physical system complexity.
3Productivity
If rapid deployment of autonomous robotics is accelerated, then productivity improves, but vulnerability to unsafe failure modes and malicious attacks increases
Solution Approach 1:
The patent implements feedback mechanisms through the transformer model that continuously analyzes configuration validity and system state. The model provides feedback on potential safety issues, configuration errors, and security vulnerabilities before deployment, enabling rapid deployment while maintaining safety through continuous validation.
Solution Approach 2:
The patent applies beforehand cushioning by incorporating safety checks, validation rules, and security protocols into the configuration generation process before deployment. The GPT model is trained to recognize and prevent unsafe configurations, providing a buffer against potential failures and attacks before they can affect the system.
Data Source
AI summary
Provided herein are system, apparatus, article of manufacture, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for artificial intelligence in mobile autonomous robotics and autonomous mobile platforms. An example aspect operates by a method of using a general-purpose robotics operating system (GPROS) with generative pre-trained transformers (GPT) (GPROS-GPT) model. The method includes training the GPROS-GPT model and querying the GPROS-GPT model to generate GPROS configuration data and service extension files. The method further includes loading the configuration data and the service extension files into a GPROS-based application and using the GPROS-based application to operate a GPROS-based robot or a GPROS-based autonomous vehicle.