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{} |
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--- |
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# ShareRobot Dataset |
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**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. |
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## Overview of ShareRobot Dataset |
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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. |
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For **Affordance**, we have 6,522 images and each with affordance areas aligned with an instruction. |
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For **Trajectory**, we have 6,870 images and each with at least 3 {x, y} coordinates aligned with an instruction. |
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## Dataset Sources |
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**ShareRobot** dataset contains 23 original datasets from Open X-Embodiment dataset [2], 12 embodiments and 107 types of atomic tasks. |
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### Raw Dataset for Planning |
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| Raw Dataset | Number of Raws | |
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|:-------------------------------------------------------------:| --------------:| |
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| nyu_door_opening_surprising_effectiveness | 421 | |
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| bridge | 15738 | |
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| dlr_edan_shared_control_converted_externally_to_rlds | 63 | |
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| utokyo_xarm_pick_and_place_converted_externally_to_rlds | 92 | |
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| cmu_stretch | 10 | |
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| asu_table_top_converted_externally_to_rlds | 109 | |
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| dlr_sara_pour_converted_externally_to_rlds | 51 | |
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| utokyo_xarm_bimanual_converted_externally_to_rlds | 27 | |
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| robo_set | 18164 | |
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| dobbe | 5200 | |
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| berkeley_autolab_ur5 | 882 | |
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| qut_dexterous_manpulation | 192 | |
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| aloha_mobile | 264 | |
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| dlr_sara_grid_clamp_converted_externally_to_rlds | 40 | |
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| ucsd_pick_and_place_dataset_converted_externally_to_rlds | 569 | |
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| ucsd_kitchen_dataset_converted_externally_to_rlds | 39 | |
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| jaco_play | 956 | |
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| utokyo_pr2_opening_fridge_converted_externally_to_rlds | 64 | |
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| conq_hose_manipulation | 56 | |
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| fmb | 7836 | |
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| plex_robosuite | 398 | |
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| utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds | 189 | |
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| viola | 44 | |
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### Raw Dataset for Affordance |
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| Raw Dataset | Number of Raws | |
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|:-------------------------------------------------------------:| -------------:| |
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| utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds | 24 | |
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| utokyo_xarm_pick_and_place_converted_externally_to_rlds | 23 | |
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| ucsd_kitchen_dataset_converted_externally_to_rlds | 10 | |
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| ucsd_pick_and_place_dataset_converted_externally_to_rlds | 112 | |
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| nyu_door_opening_surprising_effectiveness | 85 | |
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| jaco_play | 171 | |
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| bridge | 2610 | |
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| utokyo_pr2_opening_fridge_converted_externally_to_rlds | 12 | |
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| asu_table_top_converted_externally_to_rlds | 24 | |
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| viola | 1 | |
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| berkeley_autolab_ur5 | 122 | |
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| aloha_mobile | 23 | |
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| conq_hose_manipulation | 1 | |
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| dobbe | 717 | |
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| fmb | 561 | |
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| plex_robosuite | 13 | |
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| qut_dexterous_manpulation | 16 | |
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| robo_set | 1979 | |
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| dlr_edan_shared_control_converted_externally_to_rlds | 18 | |
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| **Summary** | 6522 | |
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### Raw Dataset for Trajectory |
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| Raw Dataset | Number of Raws | |
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|:-------------------------------------------------------------:| -------------:| |
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| utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds | 35 | |
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| utokyo_xarm_pick_and_place_converted_externally_to_rlds | 36 | |
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| ucsd_kitchen_dataset_converted_externally_to_rlds | 19 | |
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| dlr_sara_grid_clamp_converted_externally_to_rlds | 1 | |
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| ucsd_pick_and_place_dataset_converted_externally_to_rlds | 109 | |
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| nyu_door_opening_surprising_effectiveness | 74 | |
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| jaco_play | 175 | |
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| utokyo_xarm_bimanual_converted_externally_to_rlds | 7 | |
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| bridge | 2986 | |
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| utokyo_pr2_opening_fridge_converted_externally_to_rlds | 12 | |
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| asu_table_top_converted_externally_to_rlds | 22 | |
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| berkeley_autolab_ur5 | 164 | |
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| dobbe | 759 | |
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| fmb | 48 | |
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| qut_dexterous_manpulation | 29 | |
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| robo_set | 2374 | |
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| dlr_sara_pour_converted_externally_to_rlds | 3 | |
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| dlr_edan_shared_control_converted_externally_to_rlds | 17 | |
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| **Summary** | 6870 | |
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## Data Format |
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### Planning |
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```json |
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{ |
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"id"{ |
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"id": 0, |
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"task": "Future_Prediction_Task", |
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"selected_step": 3, |
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"conversations": [ |
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{ |
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"from": "human", |
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"value": "<image 0-25> After <move the grasped banana towards the mug>, what's the most probable next event?" |
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}, |
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{ |
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"from": "gpt", |
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"value": "<place the banana into the mug>" |
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} |
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], |
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"image": [ |
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"/path/to/image_0-25" |
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] |
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} |
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} |
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``` |
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### Affordance |
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<!----> |
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<div style="display: flex; gap: 10px;"> |
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<img src="./images/2d94d985-d47e-4899-9760-c1cb8f19cd89.png" style="width: 300px;" /> |
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<img src="./images/a7817c0b-04b1-4a7c-9535-f9ff7801a689.png" style="width: 300px;" /> |
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</div> |
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```json |
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{ |
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"id": 2486, |
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"meta_data": { |
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"original_dataset": "bridge", |
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"original_width": 640, |
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"original_height": 480 |
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}, |
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"instruction": "place the red fork to the left of the left burner", |
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"affordance": { |
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"x": 352.87425387858815, |
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"y": 186.47871614766484, |
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"width": 19.296008229513156, |
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"height": 14.472006172134865 |
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} |
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``` |
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#### Visualize Code |
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```python |
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import json |
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import os |
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import cv2 |
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import numpy as np |
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img_dir = '/path/to/your/original/images/dir' |
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affordance_json = '/path/to/your/affordances/json' |
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output_img_dir = '/path/to/your/visualized/images/dir' |
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with open(affordance_json, 'r') as f: |
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data = json.load(f) |
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for item in data: |
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filepath = os.path.join(img_dir, item['id']) |
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image = cv2.imread(filepath) |
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color = (255, 0, 0) |
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thickness = 2 |
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x_min,y_min = item['affordance']['x'], item['affordance']['y'] |
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x_max,y_max = item['affordance']['x']+item['affordance']['width'], item['affordance']['y']+item['affordance']['height'] |
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# 定义矩形的四个顶点坐标 |
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pts = np.array([ |
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[x_min, y_min], # 左上角 |
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[x_max, y_min], # 右上角 |
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[x_max, y_max], # 右下角 |
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[x_min, y_max] # 左下角 |
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], dtype=np.float32) |
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# 绘制矩形框 |
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cv2.polylines(image, [pts.astype(int)], isClosed=True, color=color, thickness=thickness) |
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# 获取相对路径并拼接目标路径 |
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relative_path = os.path.relpath(filepath, img_dir) # 获取相对于 img_dir 的相对路径 |
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output_img_path = os.path.join(output_img_dir, relative_path) # 拼接目标路径 |
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# 创建目标文件夹 |
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output_directory = os.path.dirname(output_img_path) |
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if not os.path.exists(output_directory): |
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os.makedirs(output_directory) |
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# 打印调试信息 |
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print(f"Input filepath: {filepath}") |
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print(f"Output image path: {output_img_path}") |
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print(f"Output directory: {output_directory}") |
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# 保存图像 |
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cv2.imwrite(output_img_path, image) |
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``` |
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### Trajectory |
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<!-- --> |
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<div style="display: flex; gap: 10px;"> |
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<img src="./images/5b923b31-dbbf-470f-af09-5125f5b91ab0.png" style="width: 300px;" /> |
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<img src="./images/1af4535a-acc3-4417-ae33-675f4301f560.png" style="width: 300px;" /> |
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</div> |
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```json |
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{ |
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"id": 456, |
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"meta_data": { |
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"original_dataset": "bridge", |
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"original_width": 640, |
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"original_height": 480 |
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}, |
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"instruction": "reach for the carrot", |
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"points": [ |
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[ |
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265.45454545454544, |
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120.0 |
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], |
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[ |
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275.1515151515152, |
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162.42424242424244 |
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], |
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[ |
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280.0, |
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213.33333333333331 |
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], |
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[ |
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280.0, |
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259.3939393939394 |
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] |
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] |
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}, |
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``` |
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#### Visualize Code |
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```python |
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import json |
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import os |
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from PIL import Image, ImageDraw |
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trajectory_final = '/path/to/your/trajectory_json' |
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img_dir = '/path/to/your/original/images/dir' |
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output_img_dir = '/path/to/your/visualzed/images/dir' |
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with open(trajectory_final, 'r') as f: |
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data = json.load(f) |
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for item in data: |
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filepath = os.path.join(img_dir, item['id']) |
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points = item['points'] |
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image = Image.open(filepath).convert("RGB") # 确保图像是 RGB 模式 |
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draw = ImageDraw.Draw(image) # 创建绘图对象 |
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# 定义颜色和线宽 |
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color = (255, 0, 0) # 红色 (RGB 格式) |
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thickness = 2 |
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scaled_points = [ |
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(point[0], point[1]) |
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for point in points |
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] |
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# 按照顺序连接相邻的点 |
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for i in range(len(scaled_points) - 1): |
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draw.line([scaled_points[i], scaled_points[i + 1]], fill=color, width=thickness) |
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# 获取相对路径并拼接目标路径 |
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relative_path = os.path.relpath(filepath, img_dir) |
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output_img_path = os.path.join(output_img_dir, relative_path) |
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# 创建目标文件夹 |
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output_directory = os.path.dirname(output_img_path) |
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if not os.path.exists(output_directory): |
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os.makedirs(output_directory) |
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# 打印调试信息 |
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print(f"Input filepath: {filepath}") |
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print(f"Output image path: {output_img_path}") |
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print(f"Output directory: {output_directory}") |
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# 保存图像 |
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image.save(output_img_path) |
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``` |
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## Evaluation |
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## Reference |
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[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. |
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[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. |
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## Citation |
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``` |
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@article{ji2025robobrain, |
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title={RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete}, |
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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}, |
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journal={arXiv preprint arXiv:2502.21257}, |
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year={2025} |
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} |
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``` |
