  It has been a long-standing dream to design artificial agents that explore
their environment efficiently via intrinsic motivation, similar to how children
perform curious free play. Despite recent advances in intrinsically motivated
reinforcement learning (RL), sample-efficient exploration in object
manipulation scenarios remains a significant challenge as most of the relevant
information lies in the sparse agent-object and object-object interactions. In
this paper, we propose to use structured world models to incorporate relational
inductive biases in the control loop to achieve sample-efficient and
interaction-rich exploration in compositional multi-object environments. By
planning for future novelty inside structured world models, our method
generates free-play behavior that starts to interact with objects early on and
develops more complex behavior over time. Instead of using models only to
compute intrinsic rewards, as commonly done, our method showcases that the
self-reinforcing cycle between good models and good exploration also opens up
another avenue: zero-shot generalization to downstream tasks via model-based
planning. After the entirely intrinsic task-agnostic exploration phase, our
method solves challenging downstream tasks such as stacking, flipping, pick &
place, and throwing that generalizes to unseen numbers and arrangements of
objects without any additional training.
