--- # For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/datasets-cards {} --- # ShareRobot Dataset **ShareRobot**, a high-quality heterogeneous dataset that labels multi-dimensional information, including task planning, object affordance, and end-effector trajectory, effectively enhancing various robotic capabilities. ## Overview of ShareRobot Dataset ![ee709e8b-6f05-428d-abff-2578914aeb0d](./images/ee709e8b-6f05-428d-abff-2578914aeb0d.png) For **planning**, we have 51,403 episodes and each with 30 frames. In the process of data generation, we design 5 different templates for each of the 10 question types in RoboVQA [1]. In the process of data generation, we randomly select 2 templates of each question type to generate question-answer pairs for every instance. This process transforms 51,403 instances into 1,027,990 question-answer pairs, with annotators monitoring data generation to maintain the dataset’s integrity. For **Affordance**, we have 6,522 images and each with affordance areas aligned with an instruction. For **Trajectory**, we have 6,870 images and each with at least 3 {x, y} coordinates aligned with an instruction. ## Dataset Sources ![a608d080-665a-4ab1-bd8f-d5bd121454da](./images/a608d080-665a-4ab1-bd8f-d5bd121454da.png) **ShareRobot** dataset contains 23 original datasets from Open X-Embodiment dataset [2], 12 embodiments and 107 types of atomic tasks. ### Raw Dataset for Planning | Raw Dataset | Number of Raws | |:-------------------------------------------------------------:| --------------:| | nyu_door_opening_surprising_effectiveness | 421 | | bridge | 15738 | | dlr_edan_shared_control_converted_externally_to_rlds | 63 | | utokyo_xarm_pick_and_place_converted_externally_to_rlds | 92 | | cmu_stretch | 10 | | asu_table_top_converted_externally_to_rlds | 109 | | dlr_sara_pour_converted_externally_to_rlds | 51 | | utokyo_xarm_bimanual_converted_externally_to_rlds | 27 | | robo_set | 18164 | | dobbe | 5200 | | berkeley_autolab_ur5 | 882 | | qut_dexterous_manpulation | 192 | | aloha_mobile | 264 | | dlr_sara_grid_clamp_converted_externally_to_rlds | 40 | | ucsd_pick_and_place_dataset_converted_externally_to_rlds | 569 | | ucsd_kitchen_dataset_converted_externally_to_rlds | 39 | | jaco_play | 956 | | utokyo_pr2_opening_fridge_converted_externally_to_rlds | 64 | | conq_hose_manipulation | 56 | | fmb | 7836 | | plex_robosuite | 398 | | utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds | 189 | | viola | 44 | ### Raw Dataset for Affordance | Raw Dataset | Number of Raws | |:-------------------------------------------------------------:| -------------:| | utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds | 24 | | utokyo_xarm_pick_and_place_converted_externally_to_rlds | 23 | | ucsd_kitchen_dataset_converted_externally_to_rlds | 10 | | ucsd_pick_and_place_dataset_converted_externally_to_rlds | 112 | | nyu_door_opening_surprising_effectiveness | 85 | | jaco_play | 171 | | bridge | 2610 | | utokyo_pr2_opening_fridge_converted_externally_to_rlds | 12 | | asu_table_top_converted_externally_to_rlds | 24 | | viola | 1 | | berkeley_autolab_ur5 | 122 | | aloha_mobile | 23 | | conq_hose_manipulation | 1 | | dobbe | 717 | | fmb | 561 | | plex_robosuite | 13 | | qut_dexterous_manpulation | 16 | | robo_set | 1979 | | dlr_edan_shared_control_converted_externally_to_rlds | 18 | | **Summary** | 6522 | ### Raw Dataset for Trajectory | Raw Dataset | Number of Raws | |:-------------------------------------------------------------:| -------------:| | utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds | 35 | | utokyo_xarm_pick_and_place_converted_externally_to_rlds | 36 | | ucsd_kitchen_dataset_converted_externally_to_rlds | 19 | | dlr_sara_grid_clamp_converted_externally_to_rlds | 1 | | ucsd_pick_and_place_dataset_converted_externally_to_rlds | 109 | | nyu_door_opening_surprising_effectiveness | 74 | | jaco_play | 175 | | utokyo_xarm_bimanual_converted_externally_to_rlds | 7 | | bridge | 2986 | | utokyo_pr2_opening_fridge_converted_externally_to_rlds | 12 | | asu_table_top_converted_externally_to_rlds | 22 | | berkeley_autolab_ur5 | 164 | | dobbe | 759 | | fmb | 48 | | qut_dexterous_manpulation | 29 | | robo_set | 2374 | | dlr_sara_pour_converted_externally_to_rlds | 3 | | dlr_edan_shared_control_converted_externally_to_rlds | 17 | | **Summary** | 6870 | ## Data Format ### Planning ![data-demo](./images/data-demo.jpg) ```json { "id"{ "id": 0, "task": "Future_Prediction_Task", "selected_step": 3, "conversations": [ { "from": "human", "value": " After , what's the most probable next event?" }, { "from": "gpt", "value": "" } ], "image": [ "/path/to/image_0-25" ] } } ```       ### Affordance
```json { "id": 2486, "meta_data": { "original_dataset": "bridge", "original_width": 640, "original_height": 480 }, "instruction": "place the red fork to the left of the left burner", "affordance": { "x": 352.87425387858815, "y": 186.47871614766484, "width": 19.296008229513156, "height": 14.472006172134865 } ``` #### Visualize Code ```python import json import os import cv2 import numpy as np img_dir = '/path/to/your/original/images/dir' affordance_json = '/path/to/your/affordances/json' output_img_dir = '/path/to/your/visualized/images/dir' with open(affordance_json, 'r') as f: data = json.load(f) for item in data: filepath = os.path.join(img_dir, item['id']) image = cv2.imread(filepath) color = (255, 0, 0) thickness = 2 x_min,y_min = item['affordance']['x'], item['affordance']['y'] x_max,y_max = item['affordance']['x']+item['affordance']['width'], item['affordance']['y']+item['affordance']['height'] # 定义矩形的四个顶点坐标 pts = np.array([ [x_min, y_min], # 左上角 [x_max, y_min], # 右上角 [x_max, y_max], # 右下角 [x_min, y_max] # 左下角 ], dtype=np.float32) # 绘制矩形框 cv2.polylines(image, [pts.astype(int)], isClosed=True, color=color, thickness=thickness) # 获取相对路径并拼接目标路径 relative_path = os.path.relpath(filepath, img_dir) # 获取相对于 img_dir 的相对路径 output_img_path = os.path.join(output_img_dir, relative_path) # 拼接目标路径 # 创建目标文件夹 output_directory = os.path.dirname(output_img_path) if not os.path.exists(output_directory): os.makedirs(output_directory) # 打印调试信息 print(f"Input filepath: {filepath}") print(f"Output image path: {output_img_path}") print(f"Output directory: {output_directory}") # 保存图像 cv2.imwrite(output_img_path, image) ``` ### Trajectory
```json { "id": 456, "meta_data": { "original_dataset": "bridge", "original_width": 640, "original_height": 480 }, "instruction": "reach for the carrot", "points": [ [ 265.45454545454544, 120.0 ], [ 275.1515151515152, 162.42424242424244 ], [ 280.0, 213.33333333333331 ], [ 280.0, 259.3939393939394 ] ] }, ``` #### Visualize Code ```python import json import os from PIL import Image, ImageDraw trajectory_final = '/path/to/your/trajectory_json' img_dir = '/path/to/your/original/images/dir' output_img_dir = '/path/to/your/visualzed/images/dir' with open(trajectory_final, 'r') as f: data = json.load(f) for item in data: filepath = os.path.join(img_dir, item['id']) points = item['points'] image = Image.open(filepath).convert("RGB") # 确保图像是 RGB 模式 draw = ImageDraw.Draw(image) # 创建绘图对象 # 定义颜色和线宽 color = (255, 0, 0) # 红色 (RGB 格式) thickness = 2 scaled_points = [ (point[0], point[1]) for point in points ] # 按照顺序连接相邻的点 for i in range(len(scaled_points) - 1): draw.line([scaled_points[i], scaled_points[i + 1]], fill=color, width=thickness) # 获取相对路径并拼接目标路径 relative_path = os.path.relpath(filepath, img_dir) output_img_path = os.path.join(output_img_dir, relative_path) # 创建目标文件夹 output_directory = os.path.dirname(output_img_path) if not os.path.exists(output_directory): os.makedirs(output_directory) # 打印调试信息 print(f"Input filepath: {filepath}") print(f"Output image path: {output_img_path}") print(f"Output directory: {output_directory}") # 保存图像 image.save(output_img_path) ``` ## Evaluation ## Reference [1] Pierre Sermanet, Tianli Ding, Jeffrey Zhao, Fei Xia, Debidatta Dwibedi, Keerthana Gopalakrishnan, Christine Chan,Gabriel Dulac-Arnold, Sharath Maddineni, Nikhil J Joshi,et al. Robovqa: Multimodal long-horizon reasoning forrobotics. In ICRA, pages 645–652, 2024. [2] Abby O’Neill, Abdul Rehman, Abhinav Gupta, AbhiramMaddukuri, Abhishek Gupta, Abhishek Padalkar, AbrahamLee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, et al.Open x-embodiment: Robotic learning datasets and rt-xmodels. arXiv preprint arXiv:2310.08864, 2023. ## Citation ``` @article{ji2025robobrain, title={RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete}, author={Ji, Yuheng and Tan, Huajie and Shi, Jiayu and Hao, Xiaoshuai and Zhang, Yuan and Zhang, Hengyuan and Wang, Pengwei and Zhao, Mengdi and Mu, Yao and An, Pengju and others}, journal={arXiv preprint arXiv:2502.21257}, year={2025} } ```