ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learning

ArXi:2501.14622v4 Announce Type: replace Learning efficient representations for decision-making policies is a challenge in imitation learning (IL). Current IL methods require expert nstrations, which are expensive to collect. Additionally, they are not explicitly trained to understand the environment. Consequently, they have underdeveloped world models. Self-supervised learning (SSL) offers an alternative, as it can learn a world model from diverse, unlabeled data. However, most SSL methods are inefficient because they operate in raw input space.