  Uncertainties in the real world mean that is impossible for system designers
to anticipate and explicitly design for all scenarios that a robot might
encounter. Thus, robots designed like this are fragile and fail outside of
highly-controlled environments. Causal models provide a principled framework to
encode formal knowledge of the causal relationships that govern the robot's
interaction with its environment, in addition to probabilistic representations
of noise and uncertainty typically encountered by real-world robots. Combined
with causal inference, these models permit an autonomous agent to understand,
reason about, and explain its environment. In this work, we focus on the
problem of a robot block-stacking task due to the fundamental perception and
manipulation capabilities it demonstrates, required by many applications
including warehouse logistics and domestic human support robotics. We propose a
novel causal probabilistic framework to embed a physics simulation capability
into a structural causal model to permit robots to perceive and assess the
current state of a block-stacking task, reason about the next-best action from
placement candidates, and generate post-hoc counterfactual explanations. We
provide exemplar next-best action selection results and outline planned
experimentation in simulated and real-world robot block-stacking tasks.
