从观察序列学习任务模型，让机器人自动分解复杂任务并验证执行
LATMOS: Latent Automaton Task Model from Observation Sequences
- 用自动机理论在隐空间构建任务状态机，结合观测编码器提取特征
- 在三类任务中均提升规划与验证效果，跨模态表现稳定
- 适合做机器人自主任务规划的研究者和开发者参考
从高层指令进行机器人任务规划是实现服务领域全自主机器人的关键一步。当前面临三大挑战：（i）将复杂任务规范分解为可执行的子任务；（ii）从原始观测中理解当前任务状态；（iii）任务执行的规划与验证。为此，我们提出LATMOS——一种受自动机启发的任务模型，该模型在给定正确任务执行的观测序列基础上，能够完成任务分解，并支持规划与验证操作。LATMOS结合观测编码器以提取高维观测中的特征，利用自动机理论学习一个封装了符号化状态转移的序列模型，其状态符号位于隐空间中。我们在三种任务建模设置下进行了广泛评估：（i）由逻辑公式描述的抽象任务；（ii）由视频和自然语言提示描述的真实人类任务；（iii）由图像与状态观测描述的机器人任务。结果表明，LATMOS在不同观测模态和任务类型下均显著提升了规划生成与验证能力。
原文摘要 · Abstract (English)
Robot task planning from high-level instructions is an important step towards deploying fully autonomous robot systems in the service sector. Three key aspects of robot task planning present challenges yet to be resolved simultaneously, namely, (i) factorization of complex tasks specifications into simpler executable subtasks, (ii) understanding of the current task state from raw observations, and (iii) planning and verification of task executions. To address these challenges, we propose LATMOS, an automata-inspired task model that, given observations from correct task executions, is able to factorize the task, while supporting verification and planning operations. LATMOS combines an observation encoder to extract the features from potentially high-dimensional observations with automata theory to learn a sequential model that encapsulates an automaton with symbols in the latent feature space. We conduct extensive evaluations in three task model learning setups: (i) abstract tasks described by logical formulas, (ii) real-world human tasks described by videos and natural language prompts and (iii) a robot task described by image and state observations. The results demonstrate the improved plan generation and verification capabilities of LATMOS across observation modalities and tasks.
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