  In order to provide adaptive and user-friendly solutions to robotic
manipulation, it is important that the agent can learn to accomplish tasks even
if they are only provided with very sparse instruction signals. To address the
issues reinforcement learning algorithms face when task rewards are sparse,
this paper proposes an intrinsic motivation approach that can be easily
integrated into any standard reinforcement learning algorithm and can allow
robotic manipulators to learn useful manipulation skills with only sparse
extrinsic rewards. Through integrating and balancing empowerment and curiosity,
this approach shows superior performance compared to other state-of-the-art
intrinsic exploration approaches during extensive empirical testing.
Qualitative analysis also shows that when combined with diversity-driven
intrinsic motivations, this approach can help manipulators learn a set of
diverse skills which could potentially be applied to other more complicated
manipulation tasks and accelerate their learning process.
