  Embodied agents capable of complex physical skills can improve productivity,
elevate life quality, and reshape human-machine collaboration. We aim at
autonomous training of embodied agents for various tasks involving mainly large
foundation models. It is believed that these models could act as a brain for
embodied agents; however, existing methods heavily rely on humans for task
proposal and scene customization, limiting the learning autonomy, training
efficiency, and generalization of the learned policies. In contrast, we
introduce a brain-body synchronization ({\it BBSEA}) scheme to promote embodied
learning in unknown environments without human involvement. The proposed
combines the wisdom of foundation models (``brain'') with the physical
capabilities of embodied agents (``body''). Specifically, it leverages the
``brain'' to propose learnable physical tasks and success metrics, enabling the
``body'' to automatically acquire various skills by continuously interacting
with the scene. We carry out an exploration of the proposed autonomous learning
scheme in a table-top setting, and we demonstrate that the proposed
synchronization can generate diverse tasks and develop multi-task policies with
promising adaptability to new tasks and configurations. We will release our
data, code, and trained models to facilitate future studies in building
autonomously learning agents with large foundation models in more complex
scenarios. More visualizations are available at
\href{https://bbsea-embodied-ai.github.io}{https://bbsea-embodied-ai.github.io}
