智能眼镜中的灵巧性：基于真实世界人类示范的多指机器人操控
Dexterity from Smart Lenses: Multi-Fingered Robot Manipulation with In-the-Wild Human Demonstrations
AI总结：
本文提出AINA框架，通过智能眼镜采集真实世界人类示范数据，实现多指机器人操控策略的学习，无需依赖传统机器人数据。
摘要：
学习人类在自然环境中执行日常任务时的多指机器人策略长期以来一直是机器人学界的一个宏伟目标。实现这一目标将标志着在人类环境中实现通用机器人操控的重要进展，因为它将减少对劳动密集型机器人数据收集的依赖。尽管已有大量努力，但这一目标的进展一直受到人与机器人之间体感差距以及从真实世界人类视频中提取相关上下文和运动线索的困难所限制。我们主张，通过简单但足够强大的硬件获取人类数据以及我们提出的框架AINA，我们已经离实现这一梦想迈出了重要一步。AINA能够从任何人、任何地方、任何环境收集的数据中学习多指策略，使用Aria Gen 2眼镜。这些眼镜轻便便携，具有高分辨率RGB摄像头，提供准确的机载3D头部和手部姿态，且提供宽视场，可用于场景深度估计。这种设置使学习到的3D点策略能够对背景变化具有鲁棒性，并且可以直接部署，无需任何机器人数据（包括在线修正、强化学习或模拟）。我们比较了我们的框架与先前的人类到机器人策略学习方法，剖析了我们的设计选择，并在九个日常操控任务上展示了结果。机器人演示最佳在我们的网站上观看：https://aina-robot.github.io。
英文摘要：
Learning multi-fingered robot policies from humans performing daily tasks in natural environments has long been a grand goal in the robotics community. Achieving this would mark significant progress toward generalizable robot manipulation in human environments, as it would reduce the reliance on labor-intensive robot data collection. Despite substantial efforts, progress toward this goal has been bottle-necked by the embodiment gap between humans and robots, as well as by difficulties in extracting relevant contextual and motion cues that enable learning of autonomous policies from in-the-wild human videos. We claim that with simple yet sufficiently powerful hardware for obtaining human data and our proposed framework AINA, we are now one significant step closer to achieving this dream. AINA enables learning multi-fingered policies from data collected by anyone, anywhere, and in any environment using Aria Gen 2 glasses. These glasses are lightweight and portable, feature a high-resolution RGB camera, provide accurate on-board 3D head and hand poses, and offer a wide stereo view that can be leveraged for depth estimation of the scene. This setup enables the learning of 3D point-based policies for multi-fingered hands that are robust to background changes and can be deployed directly without requiring any robot data (including online corrections, reinforcement learning, or simulation). We compare our framework against prior human-to-robot policy learning approaches, ablate our design choices, and demonstrate results across nine everyday manipulation tasks. Robot rollouts are best viewed on our website: https://aina-robot.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
