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RoboCOIN - An Embodied Intelligence Dataset Open-Sourced by BAAI in Collaboration with Multiple Universities

Author: AI小集 · Original source: AI工具集

What is RoboCOIN

RoboCOIN is an embodied intelligence dataset released by the Beijing Academy of Artificial Intelligence in collaboration with multiple universities and enterprises. It contains over 180,000 demonstration data entries, covering 421 tasks and 16 different scenarios, such as homes, businesses, and factories. The data was collected from 15 different robot platforms, including dual-arm robots, semi-humanoid robots, and humanoid robots, providing multi-view RGB and depth images as well as detailed kinematic states. RoboCOIN also constructs a hierarchical capability pyramid, with multi-resolution annotations ranging from trajectory-level concepts to frame-level kinematics, enabling models to perform structured learning.

RoboCOIN

Main Features of RoboCOIN

  • Large-Scale Dataset: Provides over 180,000 demonstration data entries, covering 421 tasks and 16 different scenarios, and supports multiple robot platforms, offering a rich data foundation for embodied intelligence research.
  • Multimodal Data: Includes multi-view RGB and depth images, as well as detailed kinematic states, supports the fusion of multiple sensor data, and meets different research needs.
  • Hierarchical Annotation System: Builds multi-resolution annotations from trajectory level to frame level, supports structured learning from global planning to precise control, and improves model generalization and adaptability.
  • CoRobot Framework: Provides the RTML quality assessment language, an automated annotation toolchain, and a multi-embodiment management platform, facilitating efficient data annotation and model training.
  • Open Source and Collaboration: The dataset, toolchain, and technical report are fully open-sourced, supporting free use by individual developers, research institutions, and enterprises, and promoting collaborative innovation in the industry.

Technical Principles of RoboCOIN

  • Multi-Platform Data Collection: Collects data from multiple robot platforms (such as dual-arm robots and semi-humanoid robots), covering different tasks and scenarios to ensure data diversity and broad applicability.
  • Multimodal Data Fusion: Integrates multiple sensor data such as RGB images, depth images, and kinematic states, providing rich information input for models and improving their understanding of environments and tasks.
  • Hierarchical Annotation Method: Adopts a hierarchical annotation system from trajectory level to frame level, helping models learn task planning and motion control at different levels and enabling structured learning from macro to micro.
  • Automated Annotation Tools: Through an automated annotation toolchain, improves data annotation efficiency and quality, reduces manual annotation costs, and accelerates dataset construction and updates.
  • Unified Management Platform: Uses the multi-embodiment management platform in the CoRobot framework to achieve unified management and scheduling of different robot platforms, supporting large-scale data collection and model training.
  • Quality Assessment Language: Introduces the RTML quality assessment language to standardize the evaluation of data annotation and model performance, ensuring data quality and model reliability.

Project Address of RoboCOIN

  • Project Official Website：https://flagopen.github.io/RoboCOIN/
  • Github Repository：https://github.com/FlagOpen/RoboCOIN
  • arXiv Technical Paper：https://arxiv.org/pdf/2511.17441

Application Scenarios of RoboCOIN

  • Home Service Robots: Helps robots better understand and execute various tasks in home environments, such as cleaning, tidying, and item handling, improving the practicality and user experience of home service robots.
  • Commercial Service Robots: In commercial scenarios such as shopping malls, hotels, and restaurants, it supports robots in completing tasks such as greeting guests, guiding, and item delivery, improving the efficiency and quality of commercial services.
  • Industrial Manufacturing: Used for task planning and operation execution of industrial robots in complex production environments, such as parts assembly and material handling, enhancing the flexibility and adaptability of industrial robots.
  • Medical Assistance: In medical scenarios, it assists robots in completing tasks such as medication delivery, ward cleaning, and rehabilitation assistance, improving the automation level and efficiency of medical services.
  • Education and Research: It provides universities and research institutions with rich data resources and experimental platforms, supports teaching and research work related to embodied intelligence, and promotes the development of artificial intelligence technology.
  • Logistics and Warehousing: In logistics centers and warehouses, it helps robots complete tasks such as cargo sorting, handling, and shelf organization, improving logistics efficiency and accuracy.