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Chapter 2 · 2023

LLM-Planner: Few-Shot Grounded Planning for Embodied Agents

Chan Hee Song, Jiaman Wu, Clayton Washington

Abstract

LLM-Planner uses LLMs for few-shot grounded planning in embodied agents, dynamically replanning based on environmental feedback to complete long-horizon tasks.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

Dynamic replanning is a Zone III superpower. LLM-Planner demonstrates that agents can adapt their plans based on environmental feedback — a capability that is essential for enterprise workflows where conditions change mid-execution. The few-shot approach is practical: you do not need thousands of examples to teach an agent to replan. A handful of well-chosen examples is sufficient.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Topics

grounded planningembodied agentsreplanningfew-shot learning