用AR简化机器人示范收集，自动生成更多示范，提升学习效率。
ARCADE: Scalable Demonstration Collection and Generation via Augmented Reality for Imitation Learning
- 通过AR让用户像日常动手一样轻松录制示范
- 单个示范可生成大量合成示范，大幅减少人工耗时
- 在真实机械臂上实现3个任务高成功率，适合家庭场景
机器人模仿学习（IL）是机器人学习中的关键技术，依赖人类示范进行训练。然而，传统方法存在示范采集难、所需示范数量庞大等问题。为此，我们提出增强现实示范采集与生成框架ARCADE，用于规模化收集机器人操作示范。该框架结合两项核心能力：1）利用AR技术使用户只需像日常操作般用手完成动作即可轻松录制示范；2）基于单个真人示范自动生成大量合成示范，显著降低用户负担和时间成本。我们在真实Fetch机器人上评估了ARCADE在三个任务上的表现：三路点到达、推移和拾取放置。使用该框架，仅通过经典的行为克隆（BC）算法便快速训练出高效策略，并在三个任务中均表现优异。此外，在真实家庭任务‘倒水’中，系统达到80%的成功率。
原文摘要 · Abstract (English)
Robot Imitation Learning (IL) is a crucial technique in robot learning, where agents learn by mimicking human demonstrations. However, IL encounters scalability challenges stemming from both non-user-friendly demonstration collection methods and the extensive time required to amass a sufficient number of demonstrations for effective training. In response, we introduce the Augmented Reality for Collection and generAtion of DEmonstrations (ARCADE) framework, designed to scale up demonstration collection for robot manipulation tasks. Our framework combines two key capabilities: 1) it leverages AR to make demonstration collection as simple as users performing daily tasks using their hands, and 2) it enables the automatic generation of additional synthetic demonstrations from a single human-derived demonstration, significantly reducing user effort and time. We assess ARCADE's performance on a real Fetch robot across three robotics tasks: 3-Waypoints-Reach, Push, and Pick-And-Place. Using our framework, we were able to rapidly train a policy using vanilla Behavioral Cloning (BC), a classic IL algorithm, which excelled across these three tasks. We also deploy ARCADE on a real household task, Pouring-Water, achieving an 80% success rate.
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