Describe, Explain, Plan And Select: Interactive Planning With Large Language Models Enables Open-world Multi-task Agents
Zihao Wang, Shaofei Cai, Guanzhou Chen, Anji Liu, Xiaojian Ma, Yitao Liang. Arxiv 2023
[Paper]
[Code]
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 extended planning phases. Furthermore, it includes a goal \(\textit{selector}\), which is a trainable module that ranks parallel candidate sub-goals based on the estimated steps of completion, consequently refining the initial plan. Our experiments mark the milestone of the first zero-shot multi-task agent that can robustly accomplish 70+ Minecraft tasks and nearly double the overall performances. Further testing reveals our method’s general effectiveness in popularly adopted non-open-ended domains as well (i.e., ALFWorld and tabletop manipulation). The ablation and exploratory studies detail how our design beats the counterparts and provide a promising update on the \(\texttt{ObtainDiamond}\) grand challenge with our approach. The code is released at https://github.com/CraftJarvis/MC-Planner.
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