构建可复现的双臂操作评测框架，助力研究真实世界双臂机器人控制难题。
DuoBench: A Reproducible Benchmark for Bimanual Manipulation in Simulation and the Real World
- 设计11个任务覆盖四类协作模式，支持仿真与真实世界复现。
- 提出分阶段评估方案，可定位早期交互与并行执行失败问题。
- 开源代码数据集与视频，适合双臂机器人、模仿学习研究者使用。
双臂机器人系统显著拓展了操作能力，但两臂协同带来额外控制复杂性和故障模式，现有基准难以有效捕捉。本文提出DuoBench，一个针对FR3 Duo平台双臂操作策略的可扩展评测框架。该框架包含11项任务，涵盖四类协调类别，在仿真中实现，并通过3D打印可复现资产部分在真实世界再现。我们提出基于阶段的评估方案，支持超越二元成败的细粒度语义故障分析，并提供所有任务的人类遥控数据集。我们在仿真和真实硬件上对多种双臂模仿学习与视觉-语言-动作策略进行评测。结果表明，当前策略在双臂操作中仍面临挑战，尤其体现在早期交互阶段、并行执行以及仿真到真实环境的迁移。DuoBench为诊断这些失败模式及未来双臂策略学习研究提供了可复现测试平台。代码、数据集与视频已公开于https://duobench.github.io/
原文摘要 · Abstract (English)
Bimanual robot systems substantially expand manipulation capabilities, but coordinating two arms introduces additional control complexity and failure modes that are not well captured by existing benchmarks. We introduce DuoBench, an extensible benchmarking framework for bimanual manipulation policies on the FR3 Duo platform. DuoBench comprises eleven tasks spanning four coordination categories, implemented in simulation and partially reproduced in the real world through reproducible task recipes with 3D-printable assets. In addition, we propose a stage-based evaluation scheme that supports fine-grained semantic failure analysis beyond binary success and provide human-teleoperated datasets for all benchmark tasks. We benchmark several dual-arm imitation-learning and vision-language-action policies in simulation and on real hardware. Our results show that current policies remain challenged by bimanual manipulation, particularly in early interaction stages, parallel arm execution, and transfer between simulation and real-world settings. DuoBench provides a reproducible testbed for diagnosing these failure modes and studying future methods for dual-arm policy learning. Code, datasets, and videos are available at https://duobench.github.io/
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