Occlusions, restricted field of view and limited resolution all constrain a robot's ability to sense its environment from a single observation. In these cases, the robot first needs to actively query multiple observations and accumulate information before it can complete a task. In this paper, we cast this problem of active vision as active inference, which states that an intelligent agent maintains a generative model of its environment and acts in order to minimize its surprise, or expected free energy according to this model. We apply this to an object-reaching task for a 7-DOF robotic manipulator with an in-hand camera to scan the workspace. A novel generative model using deep neural networks is proposed that is able to fuse multiple views into an abstract representation and is trained from data by minimizing variational free energy. We validate our approach experimentally for a reaching task in simulation in which a robotic agent starts without any knowledge about its workspace. Each step, the next view pose is chosen by evaluating the expected free energy. We find that by minimizing the expected free energy, exploratory behavior emerges when the target object to reach is not in view, and the end effector is moved to the correct reach position once the target is located. Similar to an owl scavenging for prey, the robot naturally prefers higher ground for exploring, approaching its target once located.

中文翻译：

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利用自由能原理的机器人操纵器主动视觉

遮挡、有限的视野和有限的分辨率都限制了机器人通过单次观察感知环境的能力。在这些情况下，机器人首先需要主动查询多个观察结果并积累信息，然后才能完成任务。在本文中，我们将主动视觉问题转化为主动推理，即智能体维护其环境的生成模型，并根据该模型采取行动，以尽量减少其意外或预期的自由能。我们将其应用于带有手持相机来扫描工作空间的 7 自由度机器人操纵器的物体触及任务。提出了一种使用深度神经网络的新颖生成模型，该模型能够将多个视图融合成抽象表示，并通过最小化变分自由能从数据进行训练。我们通过实验验证了我们的方法，用于模拟中的到达任务，其中机器人代理在不了解其工作空间的情况下启动。每一步，下一个视图姿势都是通过评估预期的自由能来选择的。我们发现，通过最小化预期自由能，当要到达的目标物体不在视野中时，就会出现探索行为，并且一旦定位到目标，末端执行器就会移动到正确的到达位置。与猫头鹰寻找猎物类似，机器人自然更喜欢在地势较高的地方进行探索，一旦找到目标就会接近目标。