  Current reinforcement learning (RL) in robotics often experiences difficulty
in generalizing to new downstream tasks due to the innate task-specific
training paradigm. To alleviate it, unsupervised RL, a framework that
pre-trains the agent in a task-agnostic manner without access to the
task-specific reward, leverages active exploration for distilling diverse
experience into essential skills or reusable knowledge. For exploiting such
benefits also in robotic manipulation, we propose an unsupervised method for
transferable manipulation skill discovery that ties structured exploration
toward interacting behavior and transferable skill learning. It not only
enables the agent to learn interaction behavior, the key aspect of the robotic
manipulation learning, without access to the environment reward, but also to
generalize to arbitrary downstream manipulation tasks with the learned
task-agnostic skills. Through comparative experiments, we show that our
approach achieves the most diverse interacting behavior and significantly
improves sample efficiency in downstream tasks including the extension to
multi-object, multitask problems.
