Constrained natural language action planning for resilient embodied systems

Replicating human-level intelligence in embodied task execution remains challenging due to the unconstrained nature of real-world environments. Recent use of large language models (LLMs) for task planning seeks to address the previously intractable state/action space of complex planning tasks, but hallucinations limit their viability. Additionally, the prompt engineering required for adequate system performance lacks transparency and repeatability. In contrast, symbolic planning methods offer strong reliability and repeatability guarantees, but struggle to scale to the complexity of real-world tasks. We introduce a new planning method that augments LLM planners with symbolic planning oversight to improve reliability and repeatability, and provide a transparent approach to defining hard constraints with more clarity than traditional prompt engineering. We demonstrate our approach in simulated environments, outperforming current state-of-the-art methods. Deployment of our method to a real-world quadruped robot resulted in 75% task success compared to 50% and 14.3% for pure LLM and symbolic planners across several embodied tasks, many of which required complex reasoning and interaction with humans in realistic scenarios. Our approach presents an effective strategy to enhance the reliability, repeatability, and transparency of LLM-based robot planners while retaining their key strengths: flexibility and generalizability to complex real-world environments.