Traditional AI-planning methods for task planning in robotics require a symbolically encoded domain description. While powerful in well-defined scenarios, as well as human-interpretable, setting this up requires substantial effort. Different from this, most everyday planning tasks are solved by humans intuitively, using mental imagery of the different planning steps. Here we suggest that the same approach can be used for robots, too, in cases which require only limited execution accuracy. In the current study, we propose a novel sub-symbolic method called Simulated Mental Imagery for Planning (SiMIP), which consists of perception, simulated action, success-checking and re-planning performed on 'imagined' images. We show that it is possible to implement mental imagery-based planning in an algorithmically sound way by combining regular convolutional neural networks and generative adversarial networks. With this method, the robot acquires the capability to use the initially existing scene to generate action plans without symbolic domain descriptions, while at the same time plans remain human-interpretable, different from deep reinforcement learning, which is an alternative sub-symbolic approach. We create a dataset from real scenes for a packing problem of having to correctly place different objects into different target slots. This way efficiency and success rate of this algorithm could be quantified.

翻译：传统人工智能规划方法在机器人任务规划中需要符号编码的领域描述。虽然这些方法在定义明确的场景中表现出色且具有人类可解释性，但建立此类描述需要大量工作。与此不同，大多数日常规划任务由人类通过心智想象不同规划步骤直观解决。本文提出，在仅需有限执行精度的情况下，机器人也可采用相同方法。在本研究中，我们提出一种名为“规划模拟心智想象”（SiMIP）的新型亚符号方法，该方法包括在“想象”图像上执行的感知、模拟动作、成功检查和重新规划。我们证明，通过结合常规卷积神经网络和生成对抗网络，可以在算法合理的方式下实现基于心智想象的规划。通过该方法，机器人能够利用初始场景生成动作规划，无需符号领域描述，同时规划仍保持人类可解释性——这不同于作为替代亚符号方法的深度强化学习。我们针对物体需正确放置到不同目标槽中的装箱问题，从真实场景创建数据集，从而量化该算法的效率和成功率。