Ensuring Safety in LLM-Driven Robotics: A Cross-Layer Sequence Supervision Mechanism

Integrating Large Language Models (LLMs) into robotics significantly enhances autonomous task planning. How- ever, ensuring that multi-step task plans (action sequence) generated by LLMs comply with pre-defined safety constraints during planning and execution remains a challenge, limiting their adaptability in complex environments. To address this issue, a mechanism that can monitor and adjust the plan gen- erated by the LLM-driven task planner and guide the motion planner to avoid potential risks during action execution is required. Therefore, this paper proposes a cross-layer sequence supervision mechanism. Specifically, we employ linear temporal logic syntax to express safety constraints and convert them into a set of nondeterministic B ̈uchi automatons to build a cross-layer safety supervisor. For the task planning layer, the safety supervisor provides a closed-loop correction mechanism that can identify violations in the task plan in real time and guide LLM-driven planners to correct this plan to ensure compliance. For the motion planning layer, the safety supervisor introduces virtual “obstacle” information into the task plan to form the task plan tuple. Based on this plan tuple, the motion planner can proactively prevent unsafe behaviors during action execution. Extensive experimentation demonstrates sig- nificant improvements in safety with this cross-layer supervision mechanism, highlighting its potential to enhance LLM-driven robotic technology. Experiment details can be found in https: //youtu.be/BDdSSEP6HJw.