Claude Code、Codex 这些 Agent 工具的干活方式，现在被搬到机器人身上了。
This is a AI post classified by Jev as AI agents (a tool drop), kept by the AI Radar because it carries real work, not commentary.
Claude Code、Codex 这些 Agent 工具的干活方式，现在被搬到机器人身上了。 刚刚，一套专为机器人打造的基础设施 RPent，开源了。 它没有让某一个通用模型变得无所不能，而是将具身智能拆解为不同层次的能力，让不同模型各自承担最擅长的事情。 分工明确，GPT-6 Astra 这类通用大模型负责理解任务、做计划，而抓取、插拔这类精细动作则交给专门的操作模型去执行。 GitHub：http://github.com/RLinf/RPent 成功且验证过的任务经验会沉淀下来，再次执行时可以直接复用，不用每一步都重新调用大模型。 在官方的 200 次评测里，平均执行时间从 283.6 秒降到 40.9 秒，端到端任务加速约 7 倍。 搭配 GPT-6 Astra，在 LIBERO-PRO 基准测试里任务成功率达到 92.6%。 还有一套记忆机制，引入之后，机器人在面对物体被调换位置的任务时，成功率从 31% 提到 87%。 兼容多个仿真环境，以及 Franka、YAM、SO101 等真机，想了解大模型走进物理世界能干到什么程度的，可以看看。
Posted by GitHubDaily (84.6k followers) 1 h ago · 0 likes · 766 views · view the original post on X. Kept by the AI Radar as AI agents. Tools mentioned: rpent.
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