上下文机器人学习简化：一种面向操作任务的民主化配方
In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks
- NLPR, Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所模式识别国家重点实验室）
- Amap, Alibaba Group（阿里巴巴集团高德地图）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究明确了机器人上下文学习的定义，提出极简可复现的SimpleICL框架，无需大规模预训练即可在仿真和真实环境中实现强性能，并揭示其关键特性。
AI中文摘要：
我们研究机器人上下文学习（ICL），这是一种新兴范式，使机器人能够从视觉演示中推断并执行任务。尽管其前景日益广阔，但该问题本身仍定义不足：视觉演示同时传达动作轨迹、对象语义、操作可供性、空间关系和任务目标，使得机器人实际应遵循哪些信息尚不明确。在这项工作中，我们首先提供了机器人ICL的清晰问题定义，明确其学习目标并解决这一基本提示歧义。基于此定义，我们开发了一个极简且可复现的ICL框架（SimpleICL），配备视觉提示编码器和低成本数据收集协议。无需大规模预训练或专门的数据基础设施，我们的框架在仿真和真实世界环境中均取得了强劲性能。大量实验进一步揭示了机器人ICL的几个关键特性，包括动作、语义、组合和可供性判别。我们将完全开源我们的数据和训练流程，以促进机器人ICL的系统性和可复现研究。项目页面可在此https URL找到。
英文摘要：
We study robotic in-context learning (ICL), an emerging paradigm that enables robots to infer and execute tasks from visual demonstrations. Despite its growing promise, the problem itself remains under-defined: a visual demonstration simultaneously conveys action trajectories, object semantics, manipulation affordances, spatial relations, and task goals, making it unclear what information the robot is actually expected to follow. In this work, we first provide a clear problem definition of robot ICL that explicitly defines its learning target and resolves this fundamental prompt ambiguity. Building on this definition, we develop a minimalist and reproducible ICL framework (SimpleICL) with a visual prompt encoder and a low-cost data collection protocol. Without massive pre-training or specialized data infrastructure, our framework achieves strong performance in both simulation and real-world environments. Extensive experiments further reveal several key properties of robot ICL, including semantic discrimination and task-relevant disentanglement. We will fully open-source our data and training pipeline to facilitate systematic and reproducible research on robot ICL. The project page can be found at https://simpleicl.github.io/simpleicl.
