Plug in the Safety Chip: Enforcing Constraints for LLM-driven Robot Agents (2309.09919v3)

Abstract: Recent advancements in LLMs have enabled a new research domain, LLM agents, for solving robotics and planning tasks by leveraging the world knowledge and general reasoning abilities of LLMs obtained during pretraining. However, while considerable effort has been made to teach the robot the "dos," the "don'ts" received relatively less attention. We argue that, for any practical usage, it is as crucial to teach the robot the "don'ts": conveying explicit instructions about prohibited actions, assessing the robot's comprehension of these restrictions, and, most importantly, ensuring compliance. Moreover, verifiable safe operation is essential for deployments that satisfy worldwide standards such as ISO 61508, which defines standards for safely deploying robots in industrial factory environments worldwide. Aiming at deploying the LLM agents in a collaborative environment, we propose a queryable safety constraint module based on linear temporal logic (LTL) that simultaneously enables natural language (NL) to temporal constraints encoding, safety violation reasoning and explaining, and unsafe action pruning. To demonstrate the effectiveness of our system, we conducted experiments in VirtualHome environment and on a real robot. The experimental results show that our system strictly adheres to the safety constraints and scales well with complex safety constraints, highlighting its potential for practical utility.

• The paper introduces the Safety Chip, a novel system that enforces safety constraints in LLM-driven robotics.
• It maps natural language instructions into precise LTL formulae, ensuring compliance with stringent safety standards like ISO 61508.
• Experimental results in simulated and real environments demonstrate its scalability and a 100% safety adherence rate.

Enforcing Safety Constraints in LLM-based Robot Agents

The paper addresses the challenge of ensuring safety in autonomous robotic agents driven by LLMs, a burgeoning area of research. Within this domain, the focus is shifted towards teaching robots not only the actions they should perform but also those they must avoid. Given the imperative need for compliance with global safety standards, such as ISO 61508, the authors propose a novel system named the "Safety Chip." This system is designed to enforce constraints on LLM-driven robotic agents by using Linear Temporal Logic (LTL) to map natural language instructions into enforceable safety protocols.

System Design and Key Contributions

The Safety Chip integrates a queryable safety constraint module that translates natural language instructions into LTL constraints. This module facilitates:

• Translating natural language into LTL formulae that describe prohibited actions.
• Monitoring the decision-making process of autonomous agents to prevent violations of safety constraints.
• Providing feedback through natural language to suggest re-planning when a constraint is violated.

Numerical evaluation of this system, conducted in VirtualHome environments and real-robtic platforms, demonstrated that the integrated safety module effectively adheres to safety constraints even as their complexity increases. The experimental results underscored its scalability and robustness, achieving a 100% safety rate in experiments where constraints were properly verified and deployed.

Theoretical and Practical Implications

The use of LTL in mapping language to safety constraints is particularly significant for several reasons:

1. Expressivity and Precision: LTL provides a precise method for specifying temporal task constraints, which translates effectively to many robotic planning applications.
2. Verification: LTL constraints are more straightforward to verify than the natural language descriptions, aligning with industrial standards for safety-critical systems.
3. Adaptability: The Safety Chip can function with any language understanding framework, broadening its applicability.

The implications are vast; the Safety Chip could serve as a foundational step for embedding safety as a modular component in LLM agents across various domains—ranging from household robotics to autonomous vehicles—thereby ensuring that as autonomous systems become more sophisticated, they also remain compliant with critical safety standards.

Speculation on Future Developments

Looking forward, several areas present opportunities for development. The integration of dialog systems for dynamic, real-time verification and refinement of safety constraints could be explored to improve user interaction. Additionally, incorporating neural truth value functions and extending safety chips into environments with open-vocabulary propositions could significantly enhance the robustness and flexibility of autonomous agents working in unpredictable contexts.

This research lays crucial groundwork for deploying LLM-driven robots in environments where safety is non-negotiable, providing a pathway not only for technical integration but also for meeting complex regulatory requirements. This work reflects a pragmatic approach, recognizing both the possibilities and the limitations of current LLM technology while striving towards formal safety assurances.