OCEAN：基于上下文自适应的组合任务在线推理
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation
摘要：
现实世界的任务通常表现出一种组合结构，其中包含一系列更简单的子任务。例如，开门需要伸手、抓住、旋转和拉动门把手。这样的组合任务需要一个智能体对手头的子任务进行推理，同时相应地编排全局行为。这可以作为一个在线任务推理问题，其中当前的任务身份，表示的上下文变量，估计从代理的过去的经验与概率推理。先前的方法采用简单的潜在分布，例如，高斯，为整个任务建模单个上下文。然而，这种表述缺乏表现力来捕捉子任务的组成和过渡。我们提出了一个变分推理框架OCEAN进行组合任务的在线任务推理。OCEAN在联合潜在空间中对全局和局部上下文变量进行建模，其中全局变量表示任务所需的子任务的混合，而局部变量捕获子任务之间的转换。我们的框架支持基于任务结构的先验知识的灵活潜在分布，并且可以以无监督的方式进行训练。实验结果表明，OCEAN提供了更有效的任务推理与顺序上下文自适应，从而导致复杂的，多阶段的任务的性能提升。
英文摘要：
Real-world tasks often exhibit a compositional structure that contains a sequence of simpler sub-tasks. For instance, opening a door requires reaching, grasping, rotating, and pulling the door knob. Such compositional tasks require an agent to reason about the sub-task at hand while orchestrating global behavior accordingly. This can be cast as an online task inference problem, where the current task identity, represented by a context variable, is estimated from the agent's past experiences with probabilistic inference. Previous approaches have employed simple latent distributions, e.g., Gaussian, to model a single context for the entire task. However, this formulation lacks the expressiveness to capture the composition and transition of the sub-tasks. We propose a variational inference framework OCEAN to perform online task inference for compositional tasks. OCEAN models global and local context variables in a joint latent space, where the global variables represent a mixture of sub-tasks required for the task, while the local variables capture the transitions between the sub-tasks. Our framework supports flexible latent distributions based on prior knowledge of the task structure and can be trained in an unsupervised manner. Experimental results show that OCEAN provides more effective task inference with sequential context adaptation and thus leads to a performance boost on complex, multi-stage tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
