Hindsight Planner: A Closed-loop few-shot planner for Embodied Instruction Following
LLM;Embodied Instruction Following (EIF);Agent;ALFRED
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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.
@misc{
yang2024hindsight,
title={Hindsight Planner: A Closed-loop few-shot planner for Embodied Instruction Following},
author={yuxiao Yang and Shenao Zhang and Zhihan Liu and Huaxiu Yao and Zhaoran Wang},
year={2024},
url={https://openreview.net/forum?id=grhLcNjRBq}
}
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.
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~yuxiao_Yang1 ~Shenao_Zhang1 ~Zhihan_Liu1 ~Huaxiu_Yao1 ~Zhaoran_Wang1
yuxiao Yang Shenao Zhang Zhihan Liu Huaxiu Yao Zhaoran Wang
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LLM Embodied Instruction Following (EIF) Agent ALFRED
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yang|hindsight_planner_a_closedloop_fewshot_planner_for_embodied_instruction_following
/pdf/8353589d300d84366e9c94a0b79cef7463c438d8.pdf
applications to robotics, autonomy, planning
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Hindsight Planner: A Closed-loop few-shot planner for Embodied Instruction Following
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