  Robotic manipulation stands as a largely unsolved problem despite significant
advances in robotics and machine learning in recent years. One of the key
challenges in manipulation is the exploration of the dynamics of the
environment when there is continuous contact between the objects being
manipulated. This paper proposes a model-based active exploration approach that
enables efficient learning in sparse-reward robotic manipulation tasks. The
proposed method estimates an information gain objective using an ensemble of
probabilistic models and deploys model predictive control (MPC) to plan actions
online that maximize the expected reward while also performing directed
exploration. We evaluate our proposed algorithm in simulation and on a real
robot, trained from scratch with our method, on a challenging ball pushing task
on tilted tables, where the target ball position is not known to the agent
a-priori. Our real-world robot experiment serves as a fundamental application
of active exploration in model-based reinforcement learning of complex robotic
manipulation tasks.
