Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following

• URL: http://arxiv.org/abs/2412.19562v1
• Date: Fri, 27 Dec 2024 10:05:45 GMT
• Title: Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following
• Authors: Yuxiao Yang, Shenao Zhang, Zhihan Liu, Huaxiu Yao, Zhaoran Wang,
• Abstract summary: This work focuses on building a task planner for Embodied Instruction Following (EIF) using Large Language Models (LLMs)<n>We frame the task as a Partially Observable Markov Decision Process (POMDP) and aim to develop a robust planner under a few-shot assumption.<n>Our experiments on the ALFRED dataset indicate that our planner achieves competitive performance under a few-shot assumption.
• Score: 62.10809033451526
• License: http://creativecommons.org/licenses/by/4.0/
• 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 a suboptimal action is taken, 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.

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