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不过，在贺晗看来，具身智能作为AI与物理世界交互的终极载体，正面临比通用大模型更严峻的发展瓶颈。首当其冲的便是“数据荒”。他在调研中发现，与通用大模型可借助海量互联网数据不同，具身智能需要大量“任务级、过程级”的交互数据，比如抓取、装配、搬运、开门、叠衣等，数据获取成本高、标注难。国内各研究机构和企业的数据采集平台、传感器接口、数据格式各自为战，形成了大量“数据孤岛”，缺乏具有行业共识的高质量、大规模具身智能开源数据集。，推荐阅读体育直播获取更多信息
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Less than: Every domino half in this space must add up to less than the number.