Learning a single universal policy that can perform a diverse set of
manipulation tasks is a promising new direction in robotics. However, existing
techniques are limited to learning policies that can only perform tasks that
are encountered during training, and require a large number of demonstrations
to learn new tasks. Humans, on the other hand, often can learn a new task from
a single unannotated demonstration. In this work, we propose the
Invariance-Matching One-shot Policy Learning (IMOP) algorithm. In contrast to
the standard practice of learning the end-effector's pose directly, IMOP first
learns invariant regions of the state space for a given task, and then computes
the end-effector's pose through matching the invariant regions between
demonstrations and test scenes. Trained on the 18 RLBench tasks, IMOP achieves
a success rate that outperforms the state-of-the-art consistently, by 4.5% on
average over the 18 tasks. More importantly, IMOP can learn a novel task from a
single unannotated demonstration, and without any fine-tuning, and achieves an
average success rate improvement of $11.5\%$ over the state-of-the-art on 22
novel tasks selected across nine categories. IMOP can also generalize to new
shapes and learn to manipulate objects that are different from those in the
demonstration. Further, IMOP can perform one-shot sim-to-real transfer using a
single real-robot demonstration.

通过学习不变区域和匹配测试环境中的不变区域，Invariance-Matching One-shot Policy Learning（IMOP）算法可以从单个未标注的演示中学习新任务，并取得显著的成功率提高。