Hindsight Planner: A Closed-loop few-shot planner for Embodied Instruction Following

yuxiao Yang, Shenao Zhang, Zhihan Liu, Huaxiu Yao, Zhaoran Wang

Keywords: LLM, Embodied Instruction Following (EIF), Agent, ALFRED

TL;DR: We view the ALFRED environment as a POMDP process and propose a novel hindsight method that achieves state-of-the-art performance under the few-shot assumption.

Abstract: This work focuses on building a task planner for Embodied Instruction Following (EIF) using Large Language Models (LLMs). Previous works typically train a planner to imitate expert trajectories, treating this as a supervised task. While these methods achieve competitive performance, they often lack sufficient robustness. When encountering a suboptimal action, the planner may encounter an out-of-distribution state, which can lead to task failure. In contrast, we frame the task as a Partially Observable Markov Decision Process (POMDP) and aim to develop a robust planner under a few-shot assumption. Thus, we propose a closed-loop planner with an adaptation module and a novel hindsight method, aiming to use as much information as possible to assist the planner. Our experiments on the ALFRED dataset indicate that our planner achieves competitive performance under a few-shot assumption. For the first time, our few-shot agent's performance approaches and even surpasses that of the full-shot supervised agent.

Primary Area: applications to robotics, autonomy, planning

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Submission Number: 7478