  In recent years, graph neural networks have been successfully applied for
learning the dynamics of complex and partially observable physical systems.
However, their use in the robotics domain is, to date, still limited. In this
paper, we introduce Belief Regulated Dual Propagation Networks (BRDPN), a
general-purpose learnable physics engine, which enables a robot to predict the
effects of its actions in scenes containing groups of articulated multi-part
objects. Specifically, our framework extends recently proposed propagation
networks (PropNets) and consists of two complementary components, a physics
predictor and a belief regulator. While the former predicts the future states
of the object(s) manipulated by the robot, the latter constantly corrects the
robot's knowledge regarding the objects and their relations. Our results showed
that after training in a simulator, the robot can reliably predict the
consequences of its actions in object trajectory level and exploit its own
interaction experience to correct its belief about the state of the
environment, enabling better predictions in partially observable environments.
Furthermore, the trained model was transferred to the real world and verified
in predicting trajectories of pushed interacting objects whose joint relations
were initially unknown. We compared BRDPN against PropNets, and showed that
BRDPN performs consistently well. Moreover, BRDPN can adapt its physic
predictions, since the relations can be predicted online.
