Describe, Explain, Plan And Select: Interactive Planning With Large Language Models Enables Open-world Multi-task Agents

Abstract

We investigate the challenge of task planning for multi-task embodied agents in open-world environments. Two main difficulties are identified: 1) executing plans in an open-world environment (e.g., Minecraft) necessitates accurate and multi-step reasoning due to the long-term nature of tasks, and 2) as vanilla planners do not consider how easy the current agent can achieve a given sub-task when ordering parallel sub-goals within a complicated plan, the resulting plan could be inefficient or even infeasible. To this end, we propose "$\underline\{D\}$escribe, $\underline\{E\}$xplain, $\underline\{P\}$lan and $\underline\{S\}$elect" ($\textbf\{DEPS\}$), an interactive planning approach based on Large Language Models (LLMs). DEPS facilitates better error correction on initial LLM-generated $\textit\{plan\}$ by integrating $\textit\{description\}$ of the plan execution process and providing self-$\textit\{explanation\}$ of feedback when encountering failures during the exten