Open-World Skill Discovery from Unsegmented Demonstrations
Authors: name: Jingwen Deng, name: Zihao Wang, name: Shaofei Cai, name: Anji Liu, name: Yitao Liang

Learning skills in open-world environments is essential for developing agents
capable of handling a variety of tasks by combining basic skills. Online
demonstration videos are typically long but unsegmented, making them difficult
to segment and label with skill identifiers. Unlike existing methods that rely
on sequence sampling or human labeling, we have developed a self-supervised
learning-based approach to segment these long videos into a series of
semantic-aware and skill-consistent segments. Drawing inspiration from human
cognitive event segmentation theory, we introduce Skill Boundary Detection
(SBD), an annotation-free temporal video segmentation algorithm. SBD detects
skill boundaries in a video by leveraging prediction errors from a pretrained
unconditional action-prediction model. This approach is based on the assumption
that a significant increase in prediction error indicates a shift in the skill
being executed. We evaluated our method in Minecraft, a rich open-world
simulator with extensive gameplay videos available online. Our SBD-generated
segments improved the average performance of conditioned policies by 63.7% and
52.1% on short-term atomic skill tasks, and their corresponding hierarchical
agents by 11.3% and 20.8% on long-horizon tasks. Our method can leverage the
diverse YouTube videos to train instruction-following agents. The project page
can be found in https://craftjarvis.github.io/SkillDiscovery.