用闭环自适应与事后学习，让机器人在少量样本下更稳健地执行指令。
Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following
- 将任务建模为部分可观测马尔可夫决策过程，引入闭环反馈机制。
- 少样本条件下性能接近甚至超越全样本监督模型，首次实现此突破。
- 适合研究少样本场景下具身智能任务规划的学者与开发者。
本文针对具身指令跟随（Embodied Instruction Following, EIF）任务，利用大语言模型构建任务规划器。以往方法通常通过模仿专家轨迹进行监督训练，虽表现良好但鲁棒性不足：一旦执行次优动作，系统可能进入分布外状态，导致任务失败。为此，本文将问题建模为部分可观测马尔可夫决策过程（POMDP），在少样本假设下设计一种闭环规划器，包含自适应模块和新颖的“事后学习”机制，以最大化利用环境信息辅助决策。在ALFRED数据集上的实验表明，该规划器在少样本条件下表现优异，首次使少样本代理的性能逼近并超越全样本监督代理。
原文摘要 · Abstract (English)
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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