  Pick-and-place is one of the fundamental tasks in robotics research. However,
the attention has been mostly focused on the ``pick'' task, leaving the
``place'' task relatively unexplored. In this paper, we address the problem of
placing objects in the context of a teleoperation framework. Particularly, we
focus on two aspects of the place task: stability robustness and contextual
reasonableness of object placements. Our proposed method combines
simulation-driven physical stability verification via real-to-sim and the
semantic reasoning capability of large language models. In other words, given
place context information (e.g., user preferences, object to place, and current
scene information), our proposed method outputs a probability distribution over
the possible placement candidates, considering the robustness and
reasonableness of the place task. Our proposed method is extensively evaluated
in two simulation and one real world environments and we show that our method
can greatly increase the physical plausibility of the placement as well as
contextual soundness while considering user preferences.

Robotics;Artificial Intelligence