关键变量 Go To WALL-E
自变量机器人欢迎收听自变量机器人的播客「关键变量 Go To WALL-E」。让我们一起捕捉具身智能的关键变量。 在这里，我们将分享自变量机器人的前沿思考与技术实践，无论是创始人的行业洞察，还是实验室里鲜活的研发趣事，都希望能为你带来启发。
| Publishes | Weekly | Episodes | 6 | Founded | 6 months ago |
|---|---|---|---|---|---|
| Language |  ZH-CN | Number of Listeners | Category | Technology |
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欢迎收听自变量机器人的播客「关键变量」！让我们一起捕捉具身智能的关键变量。
最近，自变量机器人联合创始人、算法合伙人甘如饴在清华大学进行了一场分享。
他在分享中提到，fancy的论文只是冰山一角，真正决定一个具身智能基础模型能力的，是冰山之下——硬件、数据、infra、后训练、评测等环节上长久的积累。
顺着这条线，他分享了更多「冰山之下」的部分：
如何“反过来”定义硬件、具身数据到底要采多少、为什么质量比数量更难、怎样的 infra 才能支撑机器人集群在线学习，后训练如何成为模型能力的「放大器」... more
欢迎收听自变量机器人的播客「关键变量」！让我们一起捕捉具身智能的关键变量。
世界模型，是今年具身智能领域最热的话题之一，围绕它的争论也最多：它和 VLA 到底是什么关系？是不是被叫错了名字？在机器人、游戏、自动驾驶这些场景里，它真的能落地吗？
在前不久我们举办的「干杯吧，具身！」线下聚会上，我们请来两位这一方向的负责人：蚂蚁灵波科技具身世界模型负责人、香港科技大学助理教授徐英豪，自变量机器人世界模型负责人 Shalfun Li，让他们面对面聊一聊，“吵一吵”。
有趣的是，两人恰好是相识十一年的本... more
欢迎收听自变量机器人的播客「关键变量」！让我们一起捕捉具身智能的关键变量。在这里，我们将分享自身的前沿思考与技术实践——无论是创始人的行业洞察，还是实验室里鲜活的研发趣事，都希望能为你带来启发。
同样是大模型，VLM 看起来已经超级聪明，但部署在真机上的具身模型却还是有点“笨笨的”，经常东西拿不稳、找不到、动作还发抖。这中间到底缺了一座什么样的桥，能把“智能”引过去？
就在最近，我们开源了具身智能基础模型 Wall-OSS-0.5，首次实现了预训练后无需针对下游任务做后训练，模型直接部署到机器人... more
欢迎收听自变量机器人的播客「关键变量」！让我们一起捕捉具身智能的关键变量。
在这期播客，自变量机器人CTO王昊和一位头部科技媒体作者进行了交流：
从最近热闹的具身智能模型“打榜”说起，展开聊聊具身智能行业模型开源、技术路径选择、「新」概念的辨析......以及更多未来趋势。
以下是时间轴：
00:45 打榜之外，为什么具身智能模型需要开源
03:44 “套壳”或基础模型，具身智能公司为什么做出不一样的选择？
09:09 先做专用模型？还是先做通用模型？ “偷懒的大模型”面前，应该采用什么样的技术... more
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