LLM-Based Safe Usage Plans for Human-in-the-Loop CPS Dynamics
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Solution Overview
Problem
Existing safety critical cyber-physical systems (CPS) with human-in-the-loop (HIL) architectures face challenges in generating personalized and safe usage plans due to increased variability and uncertainty from human inputs, which traditional safety certification processes fail to address, potentially leading to unsafe or infeasible plans.
Innovation Solution
A system utilizing a large language model (LLM) integrated with a physical dynamics coefficient estimator and reinforcement learning, specifically a liquid time constant neural network, to generate safe and feasible plans by embedding prompts with real-world traces, ensuring alignment with CPS dynamics and human safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional safety certification processes are used for HIL-CPS systems, then the system structure remains simple and easy to certify, but the system cannot adequately address the variability and uncertainty from human inputs, leading to unsafe or infeasible plans
Solution Approach 1:
The patent introduces a large language model (LLM) as an intermediary component between the human user and the CPS system. The LLM processes natural language inputs from humans, translates them into meaningful control actions, and generates usage plans that account for human variability and uncertainty. This intermediary enables the system to handle complex human inputs while maintaining safety through the HIL framework, resolving the contradiction between safety and complexity.
2Adaptability or versatility
If human inputs are incorporated into the CPS system to enable personalized control, then the system becomes more adaptive to individual users, but the variability and uncertainty increase, making traditional safety certification inadequate
Solution Approach 1:
The patent implements a dynamic safety certification framework where the LLM continuously adapts to individual user patterns and behaviors. The system learns from human inputs over time, adjusting its understanding of user preferences and constraints while maintaining safety guarantees. This dynamic approach allows the system to be personalized and adaptive without compromising reliability, as the safety certification evolves with the user rather than being static.
3Ease of manufacture
If the LLM is trained without incorporating physical system dynamics, then the training process is simpler and faster, but the generated usage plans may be infeasible for the physical system to execute
Solution Approach 1:
The patent applies preliminary action by incorporating physical system dynamics traces into the LLM training data before the LLM is deployed. During the training phase, the LLM is exposed to realistic system behaviors, constraints, and physical limitations through embedded traces from the actual CPS. This preliminary exposure ensures that when the LLM generates usage plans, they are already aligned with physical feasibility requirements, avoiding the need for complex post-generation validation.
4Productivity
If the LLM generates usage plans without safety evaluation, then the plan generation process is faster and more efficient, but the plans may violate safety criteria and harm human users
Solution Approach 1:
The patent implements a feedback mechanism where generated usage plans are evaluated against safety criteria before execution. The LLM-generated plans undergo automatic safety verification that checks compliance with predefined safety constraints and requirements. This feedback loop ensures that only safe plans are executed, preventing harmful outcomes while maintaining efficient generation speeds through automated rather than manual review processes.
Data Source
AI summary
Example implementations of a neural network implementing a large language model for generating action plans that align with physical system dynamics of a cyber-physical system (CPS) but are also safe for the human users are disclosed. Examples include a physical dynamics coefficient estimator based on a liquid time constant neural network that can derive coefficients of dynamical models with some unmeasured state variables. Further, the model coefficients are then used to train an LLM with prompts embodied with traces from dynamical system and the corresponding model coefficients. When integrated with a contextualized chatbot, feasible and safe plans can be generated to manage external events such as meals for automated insulin delivery systems used by Type 1 Diabetes subjects.