  Embodied reasoning systems integrate robotic hardware and cognitive processes
to perform complex tasks typically in response to a natural language query
about a specific physical environment. This usually involves changing the
belief about the scene or physically interacting and changing the scene (e.g.
'Sort the objects from lightest to heaviest'). In order to facilitate the
development of such systems we introduce a new simulating environment that
makes use of MuJoCo physics engine and high-quality renderer Blender to provide
realistic visual observations that are also accurate to the physical state of
the scene. Together with the simulator we propose a new benchmark composed of
10 classes of multi-step reasoning scenarios that require simultaneous visual
and physical measurements. Finally, we develop a new modular Closed Loop
Interactive Reasoning (CLIER) approach that takes into account the measurements
of non-visual object properties, changes in the scene caused by external
disturbances as well as uncertain outcomes of robotic actions. We extensively
evaluate our reasoning approach in simulation and in the real world
manipulation tasks with a success rate above 76% and 64%, respectively.
