RoboCOIN: An Open-Sourced Bimanual Robotic Data COllection for INtegrated Manipulation

Shihan Wu; Xuecheng Liu; Shaoxuan Xie; Pengwei Wang; Xinghang Li; Bowen Yang; Zhe Li; Kai Zhu; Hongyu Wu; Yiheng Liu; Zhaoye Long; Yue Wang; Chong Liu; Dihan Wang; Ziqiang Ni; Xiang Yang; You Liu; Ruoxuan Feng; Runtian Xu; Lei Zhang; Denghang Huang; Chenghao Jin; Anlan Yin; Xinlong Wang; Zhenguo Sun; Junkai Zhao; Mengfei Du; Mingyu Cao; Xiansheng Chen; Hongyang Cheng; Xiaojie Zhang; Yankai Fu; Ning Chen; Cheng Chi; Sixiang Chen; Huaihai Lyu; Xiaoshuai Hao; Yequan Wang; Bo Lei; Dong Liu; Xi Yang; Yance Jiao; Tengfei Pan; Yunyan Zhang; Songjing Wang; Ziqian Zhang; Xu Liu; Ji Zhang; Caowei Meng; Zhizheng Zhang; Jiyang Gao; Song Wang; Xiaokun Leng; Zhiqiang Xie; Zhenzhen Zhou; Peng Huang; Wu Yang; Yandong Guo; Yichao Zhu; Suibing Zheng; Hao Cheng; Xinmin Ding; Yang Yue; Huanqian Wang; Chi Chen; Jingrui Pang; YuXi Qian; Haoran Geng; Lianli Gao; Haiyuan Li; Bin Fang; Gao Huang; Yaodong Yang; Hao Dong; He Wang; Hang Zhao; Yadong Mu; Di Hu; Hao Zhao; Tiejun Huang; Shanghang Zhang; Yonghua Lin; Zhongyuan Wang; Guocai Yao

Paper

RoboCOIN: An Open-Sourced Bimanual Robotic Data COllection for INtegrated Manipulation

Abstract

Bimanual manipulation is essential for achieving human-like dexterity in robots, but the large-scale and diverse bimanual robot datasets remain scarce due to hardware heterogeneity across robotic platforms. To address the challenge, we present RoboCOIN, a comprehensive multi-embodiment bimanual manipulation dataset with over 180,000 demonstrations collected from 15 distinct robotic platforms. The dataset covers 16 scenarios, including residential, commercial, and working environments, with 421 tasks systematically organized by bimanual coordination patterns and object properties. Our key innovation is a hierarchical capability pyramid that provides multi-level annotations, spanning trajectory-level concepts, segment-level subtasks, and frame-level kinematics. We further develop CoRobot, a comprehensive processing framework featuring Robot Trajectory Markup Language (RTML) for quality assessment, automated annotation generation, and unified multi-embodiment management. Extensive experiments demonstrate the reliability and effectiveness of RoboCOIN in multi-embodiment bimanual learning, with significant performance improvements across various model architectures and robotic platforms. The complete dataset and framework are open-sourced and publicly available for further research purposes. Project website: https://FlagOpen.github.io/RoboCOIN/.