BBSEA：面向具身智能体的脑体同步探索
BBSEA: An Exploration of Brain-Body Synchronization for Embodied Agents
AI总结：
针对具身智能体学习依赖人类参与的问题，提出脑体同步方案BBSEA，利用基础模型生成任务与指标并驱动智能体自主交互学习，在桌面环境中验证了其能生成多样任务并开发出适应性强的多任务策略。
摘要：
具备复杂物理技能的具身智能体能够提高生产力、提升生活质量，并重塑人机协作。我们旨在利用大型基础模型，实现具身智能体在各类任务上的自主训练。人们普遍认为这些模型可以作为具身智能体的大脑；然而，现有方法在任务提出和场景定制上严重依赖人类，限制了学习自主性、训练效率以及所学策略的泛化能力。相比之下，我们引入了一种脑体同步（BBSEA）方案，以在无人类参与的情况下促进未知环境中的具身学习。该方法将基础模型（“大脑”）的智慧与具身智能体（“身体”）的物理能力相结合。具体而言，它利用“大脑”提出可学习的物理任务和成功指标，使“身体”能够通过与场景的持续交互自动获得各种技能。我们在桌面环境中对所提出的自主学习方案进行了探索，并证明该同步方案能够生成多样化的任务，并开发出对新任务和新配置具有良好适应性的多任务策略。我们将发布数据、代码和训练好的模型，以促进未来在更复杂场景中构建基于大型基础模型的自主学习智能体的研究。更多可视化结果请访问 https://bbsea-embodied-ai.github.io。
英文摘要：
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}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
