Abstract
Teleoperation is a cornerstone of embodied-robot learning, and bimanual dexterous teleoperation in particular provides rich demonstrations that are difficult to obtain with fully autonomous systems. While recent studies have proposed diverse hardware pipelines-ranging from inertial motion-capture gloves to exoskeletons and vision-based interfaces-there is still no unified benchmark that enables fair, reproducible comparison of these systems. In this paper, we introduce TeleOpBench, a simulator-centric benchmark tailored to bimanual dexterous teleoperation. TeleOpBench contains 30 high-fidelity task environments that span pick-and-place, tool use, and collaborative manipulation, covering a broad spectrum of kinematic and force-interaction difficulty. Within this benchmark we implement four representative teleoperation modalities-(i) MoCap, (ii) VR device, (iii) arm-hand exoskeletons, and (iv) monocular vision tracking-and evaluate them with a common protocol and metric suite. To validate that performance in simulation is predictive of real-world behavior, we conduct mirrored experiments on a physical dual-arm platform equipped with two 6-DoF dexterous hands. Across 10 held-out tasks we observe a strong correlation between simulator and hardware performance, confirming the external validity of TeleOpBench. TeleOpBench establishes a common yardstick for teleoperation research and provides an extensible platform for future algorithmic and hardware innovation.
Abstract (translated)
远程操作是具身机器人学习的基石，而双手灵巧远程操作尤其提供了难以用完全自主系统获得的丰富演示。虽然最近的研究提出了各种硬件管道——从惯性动作捕捉手套到外骨骼和基于视觉的界面——但至今仍未有一个统一的基准来公平、可重复地比较这些系统。在这篇论文中，我们介绍了TeleOpBench，这是一个以模拟器为中心的基准测试平台，专为双手灵巧远程操作设计。TeleOpBench包含30个高保真的任务环境，涵盖了抓取和放置、工具使用以及协作操纵等领域，并且覆盖了广泛的运动学和力交互难度。 在该基准中，我们实现了四种代表性的远程操作系统——(i) 动作捕捉 (MoCap)，(ii) 虚拟现实设备，(iii) 臂手外骨骼，和(iv) 单目视觉追踪，并使用统一的协议和评估指标对其进行评价。为了验证模拟器中的性能是否可以预测真实世界的行为表现，我们在配备两个6自由度灵巧机械手的物理双臂平台中进行了对称实验。在10个保留的任务上，我们观察到了模拟器与硬件性能之间存在很强的相关性，这确认了TeleOpBench的外部有效性。 TeleOpBench为远程操作研究建立了统一的标准，并提供了未来算法和硬件创新的一个可扩展平台。
URL
https://arxiv.org/abs/2505.12748
