Learning from demonstrations has shown to be an effective approach to robotic manipulation, especially with the recently collected large-scale robot data with teleoperation systems. Building an efficient teleoperation system across diverse robot platforms has become more crucial than ever. However, there is a notable lack of cost-effective and user-friendly teleoperation systems for different end-effectors, e.g., anthropomorphic robot hands and grippers, that can operate across multiple platforms. To address this issue, we develop ACE, a cross-platform visual-exoskeleton system for low-cost dexterous teleoperation. Our system utilizes a hand-facing camera to capture 3D hand poses and an exoskeleton mounted on a portable base, enabling accurate real-time capture of both finger and wrist poses. Compared to previous systems, which often require hardware customization according to different robots, our single system can generalize to humanoid hands, arm-hands, arm-gripper, and quadruped-gripper systems with high-precision teleoperation. This enables imitation learning for complex manipulation tasks on diverse platforms.

翻译：从演示中学习已被证明是机器人操作的一种有效方法，特别是随着近期通过遥操作系统收集到的大规模机器人数据。构建一个跨多样化机器人平台的高效遥操作系统变得比以往任何时候都更为关键。然而，目前明显缺乏针对不同末端执行器（例如，拟人机器人手和夹爪）的、能够跨多平台运行且具有成本效益和用户友好性的遥操作系统。为解决这一问题，我们开发了ACE，一种用于低成本灵巧遥操作的多平台视觉-外骨骼系统。我们的系统利用一个面向手部的摄像头来捕捉3D手部姿态，以及一个安装在便携式底座上的外骨骼，从而能够精确实时地捕捉手指和手腕的姿态。与以往通常需要根据不同机器人进行硬件定制的系统相比，我们的单一系统能够泛化到仿人手机器人、手臂-手机器人、手臂-夹爪机器人和四足-夹爪机器人系统，实现高精度的遥操作。这使得在多样化平台上进行复杂操作任务的模仿学习成为可能。