Mirror of X-Humanoid/RoboMIND2.0 from ModelScope. All credit to the original authors. Not an official release from them. License unchanged (apache-2.0).

🎉🎉 RoboMIND2.0 🎉🎉

中文 | English

RoboMIND2.0 has arrived. This release brings a major upgrade over RoboMIND.

We focus on complex data from dual-arm manipulation and mobile manipulation scenarios, which are highly demanded by the community. RoboMIND2.0 introduces many practical features to further advance robot learning and embodied intelligence research.

Wanna review RoboMIND1.0? Please check modelscope repo RoboMIND MS or Huggingface repo RoboMIND HF.

Key Upgrades

• RoboMIND2.0 scales up to 310K trajectories, totaling over 1,000 hours of data.
• RoboMIND2.0 includes 12K tactile-enriched sequences.
• RoboMIND2.0 collects 20K mobile manipulation trajectories.
• RoboMIND2.0 covers 6 popular robotic embodiments.

Embodiments Involved 🤖

Tienkung	Tianyi	AgileX
Franka	UR	Ark

Sub-repository Links

• Tienkung

  • X-Humanoid/RoboMIND2.0-Tienkung

• Tianyi

  • X-Humanoid/RoboMIND2.0-Tianyi

• Tianyi (Mobile)

  • X-Humanoid/RoboMIND2.0-Tianyi-mobile

• AgileX

  • X-Humanoid/RoboMIND2.0-Agilex

• AgileX (Mobile)

  • X-Humanoid/RoboMIND2.0-Agilex-mobile

• Franka

  • X-Humanoid/RoboMIND2.0-Franka-Part-1
  • X-Humanoid/RoboMIND2.0-Franka-Part-2
  • X-Humanoid/RoboMIND2.0-Franka-Part-3
  • X-Humanoid/RoboMIND2.0-Franka-Part-4
  • X-Humanoid/RoboMIND2.0-Franka-Part-5

• UR

  • X-Humanoid/RoboMIND2.0-UR5

• UR + DexHand

  • X-Humanoid/RoboMIND2.0-UR5-Dex

• Ark

  • X-Humanoid/RoboMIND2.0-Ark

• Ark (Mobile)

  • X-Humanoid/RoboMIND2.0-Ark-mobile

• Simulation

  • X-Humanoid/RoboMIND2.0-Tienkung-sim
  • X-Humanoid/RoboMIND2.0-Franka-sim

Overall HDF5 Data Structure

Below is the unified HDF5 format used in RoboMIND2.0. Each dataset variant contains a subset of the following structure.

/
├── metadata
│   ├── language_instruction
│   ├── data_type
│   ├── data_format_version
│   ├── collection_time
│   ├── collector
│   ├── sim_assets
│   └── trajectory_length
│
├── camera_model
│   ├── camera_top
│   ├── camera_front
│   ├── camera_left
│   ├── camera_right
│   ├── camera_head
│   ├── camera_wrist_left
│   └── camera_wrist_right
│
├── camera_color_resolution
│   ├── camera_top
│   ├── camera_front
│   ├── camera_left
│   ├── camera_right
│   ├── camera_head
│   ├── camera_wrist_left
│   └── camera_wrist_right
│
├── camera_color_channel
│   ├── camera_top
│   ├── camera_front
│   ├── camera_left
│   ├── camera_right
│   ├── camera_head
│   ├── camera_wrist_left
│   └── camera_wrist_right
│
├── camera_depth_resolution
│   ├── camera_top
│   ├── camera_front
│   ├── camera_left
│   ├── camera_right
│   ├── camera_head
│   ├── camera_wrist_left
│   └── camera_wrist_right
│
├── camera_intrinsics
│   ├── camera_top
│   │   ├── matrix
│   │   └── dist_coeffs
│   ├── camera_front
│   │   ├── matrix
│   │   └── dist_coeffs
│   ├── camera_left
│   │   ├── matrix
│   │   └── dist_coeffs
│   ├── camera_right
│   │   ├── matrix
│   │   └── dist_coeffs
│   ├── camera_head
│   │   ├── matrix
│   │   └── dist_coeffs
│   ├── camera_wrist_left
│   │   ├── matrix
│   │   └── dist_coeffs
│   └── camera_wrist_right
│       ├── matrix
│       └── dist_coeffs
│
├── camera_extrinsics
│   ├── camera_top
│   ├── camera_front
│   ├── camera_left
│   ├── camera_right
│   ├── camera_head
│   ├── camera_wrist_left
│   └── camera_wrist_right
│
├── base_to_robot_transformation
│   ├── base_to_robot_left
│   ├── base_to_robot_right
│   └── base_to_robot_single
│
├── force_model
│   ├── force_left_end_effector
│   ├── force_right_end_effector
│   ├── force_left_arm_joints
│   └── force_right_arm_joints
│
├── tactile_model
│   ├── tactile_left
│   └── tactile_right
│
├── master
│   ├── arm_left_position_raw
│   │   ├── timestamp
│   │   ├── is_intervene
│   │   └── data
│   ├── arm_left_position_align
│   ├── arm_right_position_raw
│   ├── arm_right_position_align
│   ├── arm_single_position_raw
│   ├── arm_single_position_align
│   ├── end_effector_left_position_raw
│   ├── end_effector_left_position_align
│   ├── end_effector_right_position_raw
│   ├── end_effector_right_position_align
│   ├── end_effector_single_position_raw
│   ├── end_effector_single_position_align
│   ├── end_effector_left_pose_raw
│   ├── end_effector_left_pose_align
│   ├── end_effector_right_pose_raw
│   ├── end_effector_right_pose_align
│   ├── end_effector_single_pose_raw
│   ├── end_effector_single_pose_align
│   ├── head_position_raw
│   ├── head_position_align
│   ├── waist_position_raw
│   ├── waist_position_align
│   ├── leg_left_position_raw
│   ├── leg_left_position_align
│   ├── leg_right_position_raw
│   ├── leg_right_position_align
│   ├── chassis_pose_raw
│   ├── chassis_pose_align
│   ├── chassis_twist_raw
│   └── chassis_twist_align
│
├── puppet
│   └── (same structure as master)
│
├── camera_observations
│   ├── timestamp
│   ├── is_intervene
│   ├── color_images
│   │   ├── camera_top
│   │   ├── camera_front
│   │   ├── camera_left
│   │   ├── camera_right
│   │   ├── camera_head
│   │   ├── camera_wrist_left
│   │   └── camera_wrist_right
│   └── depth_images
│       ├── camera_top
│       ├── camera_front
│       ├── camera_left
│       ├── camera_right
│       ├── camera_head
│       ├── camera_wrist_left
│       └── camera_wrist_right
│
├── force_observations
│   ├── force_left_end_effector_raw
│   ├── force_left_end_effector_align
│   ├── force_right_end_effector_raw
│   ├── force_right_end_effector_align
│   ├── force_left_arm_joints_raw
│   ├── force_left_arm_joints_align
│   ├── force_right_arm_joints_raw
│   └── force_right_arm_joints_align
│
└── tactile_observations
    ├── tactile_left_raw
    ├── tactile_left_align
    ├── tactile_right_raw
    └── tactile_right_align

RoboMIND-Sim

Additionally, we have open-sourced a simulation environment built on Isaac Sim, which includes training datasets and standardized evaluation scripts. Researchers can leverage these resources out-of-the-box to rapidly conduct model performance evaluation and algorithm validation within the simulation. RoboMIND-Sim

Discussion:

Citation

If you find this project helpful, please cite:

@misc{hou2025robomind20multimodalbimanual,
      title={RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence}, 
      author={Chengkai Hou and Kun Wu and Jiaming Liu and Zhengping Che and Di Wu and Fei Liao and Guangrun Li and Jingyang He and Qiuxuan Feng and Zhao Jin and Chenyang Gu and Zhuoyang Liu and Nuowei Han and Xiangju Mi and Yaoxu Lv and Yankai Fu and Gaole Dai and Langzhe Gu and Tao Li and Yuheng Zhang and Yixue Zhang and Xinhua Wang and Shichao Fan and Meng Li and Zhen Zhao and Ning Liu and Zhiyuan Xu and Pei Ren and Junjie Ji and Haonan Liu and Kuan Cheng and Shanghang Zhang and Jian Tang},
      year={2025},
      eprint={2512.24653},
      archivePrefix={arXiv},
      url={https://arxiv.org/abs/2512.24653}, 
}