arXiv:2506.23351cs.ROcs.AI2025-06被引 14

17项双臂操作任务挑战,推动通用双臂协作智能发展

Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop

  • 构建仿真与真实世界双轨评测体系,覆盖刚性/柔性/触觉任务
  • 64支队伍参与,生成如SEM、AnchorDP3等高性能双臂策略
  • 为可泛化的双臂智能提供基准和未来研究方向

具身人工智能是机器人学的前沿领域,亟需能在复杂物理环境中感知、推理并行动的自主系统。尽管单臂系统已表现优异,但处理刚性、柔性及触觉敏感物体的复杂任务仍需双臂协同。为此,我们在CVPR 2025 MEIS Workshop发起RoboTwin双臂协作挑战赛,基于RoboTwin仿真平台(1.0与2.0)及AgileX COBOT-Magic机器人平台,设置仿真初赛、复赛与最终真实世界赛三阶段,共涵盖17项双臂操作任务,涵盖刚性、柔性与触觉场景。活动吸引全球64支队伍、超400名参与者,涌现出SEM、AnchorDP3等顶尖解决方案,揭示了可泛化双臂策略学习的关键洞见。本报告详述竞赛架构、任务设计、评估方法、核心发现与未来方向,旨在推动鲁棒且通用的双臂操作策略研究。挑战赛官网:https://robotwin-benchmark.github.io/cvpr-2025-challenge/

原文摘要 · Abstract (English)

Embodied Artificial Intelligence (Embodied AI) is an emerging frontier in robotics, driven by the need for autonomous systems that can perceive, reason, and act in complex physical environments. While single-arm systems have shown strong task performance, collaborative dual-arm systems are essential for handling more intricate tasks involving rigid, deformable, and tactile-sensitive objects. To advance this goal, we launched the RoboTwin Dual-Arm Collaboration Challenge at the 2nd MEIS Workshop, CVPR 2025. Built on the RoboTwin Simulation platform (1.0 and 2.0) and the AgileX COBOT-Magic Robot platform, the competition consisted of three stages: Simulation Round 1, Simulation Round 2, and a final Real-World Round. Participants totally tackled 17 dual-arm manipulation tasks, covering rigid, deformable, and tactile-based scenarios. The challenge attracted 64 global teams and over 400 participants, producing top-performing solutions like SEM and AnchorDP3 and generating valuable insights into generalizable bimanual policy learning. This report outlines the competition setup, task design, evaluation methodology, key findings and future direction, aiming to support future research on robust and generalizable bimanual manipulation policies. The Challenge Webpage is available at https://robotwin-benchmark.github.io/cvpr-2025-challenge/.

双臂协作具身智能机器人挑战泛化策略

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