让机器人像聊天机器人一样自主完成复杂任务，还能从经验中学习。
ETA: A New Agentic Paradigm for Embodied Tasks
- 用规划器逐步调用工具，实现可控制、可验证的任务执行
- 在真实机器人和仿真环境中均能稳定运行，支持长期任务与经验回放
- 开源框架支持模块化设计，适合研究具身智能的开发者
机器人何时才能迎来类似ChatGPT的突破？这需要一个能处理陌生任务、在陌生环境中运行、长期交互可控且能持续学习的通用机器人。当前的具身系统大多采用端到端的观看到动作路径，尽管进展迅速，仍远未达到目标：泛化能力严重依赖训练数据覆盖范围，长时间任务执行难以控制与审查。为此，我们提出具身任务代理（ETA）新范式，将数字代理扩展至物理世界，并发布其开源实现OpenETA。ETA以规划器为核心，每次选择一个工具调用，通过接口控制执行，世界返回结果与新观测。该循环使代理能验证结果、调整计划，并将成功与失败交互转化为可复用的经验。OpenETA提供可替换的规划器、可组合的工具与技能、可审计的记忆、可回放的轨迹，以及仿真与真实机器人通用接口。对于Codex，OpenETA可作为轻量级插件，仅暴露observe、mark_point、move_to三个接口。
原文摘要 · Abstract (English)
When will robots have their ChatGPT moment? Such a breakthrough requires a general-purpose robot that can handle unfamiliar tasks in unfamiliar environments, remain controllable over long interactions, and learn from experience. Today's embodied systems largely follow an end-to-end observation-to-action path. Despite rapid progress, they remain far from this goal: their generalization depends heavily on the coverage of robot training data, while long task execution remains difficult to control and inspect. To realize this goal, we introduce the Embodied Task Agent (ETA), a new paradigm for extending digital agents into the physical world, and release OpenETA as its open-source implementation. ETA centers the robot around a Planner that chooses one Tool call at a time, an Interface that controls execution, and a World that returns the result and a fresh observation. This loop allows the agent to verify outcomes, adapt its plan, and turn successful and failed interactions into reusable experience. OpenETA provides replaceable Planners, composable Tools and Skills, auditable memory, replayable trajectories, and common interfaces for simulation and real robots. For Codex, OpenETA can operate as a lightweight plugin that exposes only observe, mark_point, and move_to.
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