  Discovering the symbols and rules that can be used in long-horizon planning
from a robot's unsupervised exploration of its environment and continuous
sensorimotor experience is a challenging task. The previous studies proposed
learning symbols from single or paired object interactions and planning with
these symbols. In this work, we propose a system that learns rules with
discovered object and relational symbols that encode an arbitrary number of
objects and the relations between them, converts those rules to Planning Domain
Description Language (PDDL), and generates plans that involve affordances of
the arbitrary number of objects to achieve tasks. We validated our system with
box-shaped objects in different sizes and showed that the system can develop a
symbolic knowledge of pick-up, carry, and place operations, taking into account
object compounds in different configurations, such as boxes would be carried
together with a larger box that they are placed on. We also compared our method
with the state-of-the-art methods and showed that planning with the operators
defined over relational symbols gives better planning performance compared to
the baselines.
