基于假设的地方行动选择的策略学习
Policy Learning with Hypothesis based Local Action Selection
摘要：
为了使机器人能够在未知和非结构化的环境中操作，机器人应该能够在环境的部分可观测性下操作。物体遮挡和未建模的环境是导致部分可观测性的一些因素。遇到这种情况的常见场景是在混乱中进行操作。在机器人需要定位感兴趣的物体并对其进行操纵的情况下，它需要执行一系列的整理动作来准确地检测感兴趣的物体。为了执行这样一系列的动作，机器人还需要考虑环境中物体的动态以及它们对接触的反应。这是一个不平凡的问题，因为人们不仅需要推理机器人与物体的相互作用，还需要推理接触时物体与物体的相互作用。在杂波中操纵的示例场景中，状态向量必须考虑感兴趣对象的姿态和周围环境的结构。过程模型必须考虑所有上述机器人-对象，对象-对象交互。随着场景中对象数量的增加，过程模型的复杂性呈指数级增长。这在非结构化环境中很常见。因此，试图明确地对所有对象-对象和机器人-对象交互进行建模是不合理的。在这种设置下，我们提出了一个基于假设的动作选择算法，我们构建一个假设集的可能构成的对象的兴趣，给定的当前证据在场景中，并选择基于我们当前的一组假设的动作。该假设集倾向于表示关于环境的结构和感兴趣的对象可以采取的姿势的数量的信念。代理的唯一停止标准是当关于对象的姿势的不确定性被完全解决时。
英文摘要：
For robots to be able to manipulate in unknown and unstructured environments the robot should be capable of operating under partial observability of the environment. Object occlusions and unmodeled environments are some of the factors that result in partial observability. A common scenario where this is encountered is manipulation in clutter. In the case that the robot needs to locate an object of interest and manipulate it, it needs to perform a series of decluttering actions to accurately detect the object of interest. To perform such a series of actions, the robot also needs to account for the dynamics of objects in the environment and how they react to contact. This is a non trivial problem since one needs to reason not only about robot-object interactions but also object-object interactions in the presence of contact. In the example scenario of manipulation in clutter, the state vector would have to account for the pose of the object of interest and the structure of the surrounding environment. The process model would have to account for all the aforementioned robot-object, object-object interactions. The complexity of the process model grows exponentially as the number of objects in the scene increases. This is commonly the case in unstructured environments. Hence it is not reasonable to attempt to model all object-object and robot-object interactions explicitly. Under this setting we propose a hypothesis based action selection algorithm where we construct a hypothesis set of the possible poses of an object of interest given the current evidence in the scene and select actions based on our current set of hypothesis. This hypothesis set tends to represent the belief about the structure of the environment and the number of poses the object of interest can take. The agent's only stopping criterion is when the uncertainty regarding the pose of the object is fully resolved.
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