Acquiring a diverse repertoire of general-purpose skills remains an open
challenge for robotics. In this work, we propose self-supervising control on
top of human teleoperated play data as a way to scale up skill learning. Play
has two properties that make it attractive compared to conventional task
demonstrations. Play is cheap, as it can be collected in large quantities
quickly without task segmenting, labeling, or resetting to an initial state.
Play is naturally rich, covering ~4x more interaction space than task
demonstrations for the same amount of collection time. To learn control from
play, we introduce Play-LMP, a self-supervised method that learns to organize
play behaviors in a latent space, then reuse them at test time to achieve
specific goals. Combining self-supervised control with a diverse play dataset
shifts the focus of skill learning from a narrow and discrete set of tasks to
the full continuum of behaviors available in an environment. We find that this
combination generalizes well empirically---after self-supervising on unlabeled
play, our method substantially outperforms individual expert-trained policies
on 18 difficult user-specified visual manipulation tasks in a simulated robotic
tabletop environment. We additionally find that play-supervised models, unlike
their expert-trained counterparts, are more robust to perturbations and exhibit
retrying-till-success behaviors. Finally, we find that our agent organizes its
latent plan space around functional tasks, despite never being trained with
task labels. Videos, code and data are available at
learning-from-play.github.io

该论文提出了基于自我监督控制和玩耍数据的方法来扩大技能学习，结合自我监督控制和多样性玩耍数据集，实现了对环境中所有可用行为的连续学习，取得了在机器人桌面环境中 18 项视觉操作任务上的显著表现，同时表现出更强的抗干扰能力和重试成功的行为。