AI 中文总结
本文提出ENCORE，一种少样本智能体方法，通过将演示提炼为多视角关键帧等证据包，让编码智能体编写策略程序，在LIBERO-PRO和真实双臂机器人上显著优于基线。
AI 中文摘要
编码智能体现在可以在很少人工帮助的情况下编写、运行和调试程序。然而，机器人任务通常由一个句子指定，该句子省略了如何抓取、以何种顺序接触以及结果应该是什么样子，而仅给定句子的智能体必须通过试错来找到这些细节。我们引入了ENCORE，它给智能体提供少量演示作为证据来阅读，而不是作为训练数据。一个确定性的构建器将每个演示提炼成多视角关键帧、夹爪事件、帧条和完整轨迹的包。编码智能体研究该包，针对固定的感知和动作API编写策略程序，在几次开发滚动中迭代改进它，并在密封评估之前冻结它，该评估从不揭示成功信号。在LIBERO-PRO上，智能体的第一个程序在带有演示的扰动任务中已经成功了一半，在一个没有演示的任务中也成功了，并且冻结的程序优于使用相同语言模型运行的最强先前智能体系统（96.3%对比89.3%）。在指令未说明目标的RoboDojo任务上，没有演示的程序无法成功。ENCORE还在真实双臂机器人上运行，从每个五个演示中学习立方体交接和杯子倒置。
英文摘要
Coding agents can now write, run, and debug programs with little human help. Robot tasks, however, are usually specified by a sentence that leaves out how to grasp, in what order to make contact, and what the result should look like, and an agent given only the sentence must find these details by trial and error. We introduce ENCORE, which gives the agent a few demonstrations as evidence to read rather than as training data. A deterministic builder distills each demonstration into a pack of multi-view keyframes, gripper events, frame strips, and the full trajectory. A coding agent studies the pack, writes a policy program against a fixed perception and action API, refines it iteratively over a few development rollouts, and freezes it before a sealed evaluation that never reveals the success signal. On LIBERO-PRO, the agent's first program already succeeds in half of the perturbed tasks with demonstrations and in one task without them, and the frozen programs outperform the strongest prior agentic system run with the same language model (96.3% against 89.3%). On RoboDojo tasks whose instructions leave the goal unstated, no program succeeds without demonstrations. ENCORE also runs on a real bimanual robot, learning cube handover and cup inversion from five demonstrations each.
