  Safe and efficient object manipulation is a key enabler of many real-world
robot applications. However, this is challenging because robot operation must
be robust to a range of sensor and actuator uncertainties. In this paper, we
present a physics-informed causal-inference-based framework for a robot to
probabilistically reason about candidate actions in a block stacking task in a
partially observable setting. We integrate a physics-based simulation of the
rigid-body system dynamics with a causal Bayesian network (CBN) formulation to
define a causal generative probabilistic model of the robot decision-making
process. Using simulation-based Monte Carlo experiments, we demonstrate our
framework's ability to successfully: (1) predict block tower stability with
high accuracy (Pred Acc: 88.6%); and, (2) select an approximate next-best
action for the block stacking task, for execution by an integrated robot
system, achieving 94.2% task success rate. We also demonstrate our framework's
suitability for real-world robot systems by demonstrating successful task
executions with a domestic support robot, with perception and manipulation
sub-system integration. Hence, we show that by embedding physics-based causal
reasoning into robots' decision-making processes, we can make robot task
execution safer, more reliable, and more robust to various types of
uncertainty.
