  We propose a novel general method that finds action-grounded, discrete object
and effect categories and builds probabilistic rules over them for non-trivial
action planning. Our robot interacts with objects using an initial action
repertoire that is assumed to be acquired earlier and observes the effects it
can create in the environment. To form action-grounded object, effect, and
relational categories, we employ a binary bottleneck layer in a predictive,
deep encoder-decoder network that takes the image of the scene and the action
applied as input, and generates the resulting effects in the scene in pixel
coordinates. After learning, the binary latent vector represents action-driven
object categories based on the interaction experience of the robot. To distill
the knowledge represented by the neural network into rules useful for symbolic
reasoning, a decision tree is trained to reproduce its decoder function.
Probabilistic rules are extracted from the decision paths of the tree and are
represented in the Probabilistic Planning Domain Definition Language (PPDDL),
allowing off-the-shelf planners to operate on the knowledge extracted from the
sensorimotor experience of the robot. The deployment of the proposed approach
for a simulated robotic manipulator enabled the discovery of discrete
representations of object properties such as `rollable' and `insertable'. In
turn, the use of these representations as symbols allowed the generation of
effective plans for achieving goals, such as building towers of the desired
height, demonstrating the effectiveness of the approach for multi-step object
manipulation. Finally, we demonstrate that the system is not only restricted to
the robotics domain by assessing its applicability to the MNIST 8-puzzle domain
in which learned symbols allow for the generation of plans that move the empty
tile into any given position.
