Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929648892379136 |
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| author | Yang, Yuxiao Zhang, Shenao Liu, Zhihan Yao, Huaxiu Wang, Zhaoran |
| author_facet | Yang, Yuxiao Zhang, Shenao Liu, Zhihan Yao, Huaxiu Wang, Zhaoran |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19562 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following Yang, Yuxiao Zhang, Shenao Liu, Zhihan Yao, Huaxiu Wang, Zhaoran Artificial Intelligence Robotics 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. |
| title | Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following |
| topic | Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2412.19562 |