UniJEPA: Enhancing Robot Policy via Unified Continuous and Discrete Representation Learning

Understand

Building generalist robot policies that can handle diverse tasks in open-ended environments is a central challenge in robotics.

• To leverage knowledge from large-scale pretraining, prior work (VLA) has typically built generalist policies either on top of vision-language understanding models (VLMs) or generative models.
• However, both semantic understanding from vision-language pretraining and visual dynamics modeling from visual-generation pretraining are crucial for embodied robots.
• Recent unified models of generation and understanding have demonstrated strong capabilities in both comprehension and generation through large-scale pretraining.