  Understanding physical phenomena is a key competence that enables humans and
animals to act and interact under uncertain perception in previously unseen
environments containing novel objects and their configurations. In this work,
we consider the problem of autonomous block stacking and explore solutions to
learning manipulation under physics constraints with visual perception inherent
to the task. Inspired by the intuitive physics in humans, we first present an
end-to-end learning-based approach to predict stability directly from
appearance, contrasting a more traditional model-based approach with explicit
3D representations and physical simulation. We study the model's behavior
together with an accompanied human subject test. It is then integrated into a
real-world robotic system to guide the placement of a single wood block into
the scene without collapsing existing tower structure. To further automate the
process of consecutive blocks stacking, we present an alternative approach
where the model learns the physics constraint through the interaction with the
environment, bypassing the dedicated physics learning as in the former part of
this work. In particular, we are interested in the type of tasks that require
the agent to reach a given goal state that may be different for every new
trial. Thereby we propose a deep reinforcement learning framework that learns
policies for stacking tasks which are parametrized by a target structure.
