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arxiv: 2506.23351 · v2 · pith:UGL3W3XLnew · submitted 2025-06-29 · 💻 cs.RO · cs.AI· cs.LG· cs.MA

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

Tianxing Chen , Kaixuan Wang , Zhaohui Yang , Yuhao Zhang , Zanxin Chen , Baijun Chen , Wanxi Dong , Ziyuan Liu
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Dong Chen Tianshuo Yang Haibao Yu Xiaokang Yang Yusen Qin Zhiqiang Xie Yao Mu Ping Luo Tian Nian Weiliang Deng Yiheng Ge Yibin Liu Zixuan Li Dehui Wang Zhixuan Liang Haohui Xie Rijie Zeng Yunfei Ge Peiqing Cong Guannan He Zhaoming Han Ruocheng Yin Jingxiang Guo Lunkai Lin Tianling Xu Hongzhe Bi Xuewu Lin Tianwei Lin Shujie Luo Keyu Li Ziyan Zhao Ke Fan Heyang Xu Bo Peng Wenlong Gao Dongjiang Li Feng Jin Hui Shen Jinming Li Chaowei Cui Yu Chen Yaxin Peng Lingdong Zeng Wenlong Dong Tengfei Li Weijie Ke Jun Chen Erdemt Bao Tian Lan Tenglong Liu Jin Yang Huiping Zhuang Baozhi Jia Shuai Zhang Zhengfeng Zou Fangheng Guan Tianyi Jia Ke Zhou Hongjiu Zhang Yating Han Cheng Fang Yixian Zou Chongyang Xu Qinglun Zhang Shen Cheng Xiaohe Wang Ping Tan Haoqiang Fan Shuaicheng Liu Jiaheng Chen Chuxuan Huang Chengliang Lin Kaijun Luo Boyu Yue Yi Liu Jinyu Chen Zichang Tan Liming Deng Shuo Xu Zijian Cai Shilong Yin Hao Wang Hongshan Liu Tianyang Li Long Shi Ran Xu Huilin Xu Zhengquan Zhang Congsheng Xu Jinchang Yang Feng Xu
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classification 💻 cs.RO cs.AIcs.LGcs.MA
keywords challengedual-armbimanualgeneralizablemanipulationrobotwinroundsimulation
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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/.

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