GoalDirected Behavior under Variational Predictive Coding: Dynamic organization of Visual Attention and Working Memory Mental simulation is a critical cognitive function for goaldirected behavior because it is essential for assessing actions and their consequences. When a selfgenerated or externally specified goal is given, a sequence of actions that is most likely to attain that goal is selected among other candidates via mental simulation. Therefore, better mental simulation leads to better goaldirected action planning. However, developing a mental simulation model is challenging because it requires knowledge of self and the environment. The current paper studies how adequate goaldirected action plans of robots can be mentally generated by dynamically organizing topdown visual attention and visual working memory. For this purpose, we propose a neural network model based on variational Bayes predictive coding, where goaldirected action planning is formulated by Bayesian inference of latent intentional space. Our experimental results showed that cognitively meaningful competencies, such as autonomous topdown attention to the robot end effector (its hand) as well as dynamic organization of occlusionfree visual working memory, emerged. Furthermore, our analysis of comparative experiments indicated that the introduction of visual working memory and the inference mechanism using variational Bayes predictive coding significantly improved the performance in planning adequate goaldirected actions.

Learning PhysicsBased Manipulation in Clutter: Combining ImageBased Generalization and LookAhead Planning Physicsbased manipulation in clutter involves complex interaction between multiple objects. In this paper, we consider the problem of learning, from interaction in a physics simulator, manipulation skills to solve this multistep sequential decision making problem in the real world. Our approach has two key properties: (i) the ability to generalize and transfer manipulation skills (over the type, shape, and number of objects in the scene) using an abstract imagebased representation that enables a neural network to learn useful features; and (ii) the ability to perform lookahead planning in the image space using a physics simulator, which is essential for such multistep problems. We show, in sets of simulated and realworld experiments (video available on https:youtu.beEmkUQfyvwkY), that by learning to evaluate actions in an abstract imagebased representation of the real world, the robot can generalize and adapt to the object shapes in challenging realworld environments.