Update README.md

README.md

@@ -9,125 +9,336 @@
 **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
-
-<!-- Provide the basic links for the dataset. -->
-
-<!-- - **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
- -->
-## Uses
-
-<!-- Address questions around how the dataset is intended to be used. -->
-
-### Direct Use
-
-<!-- This section describes suitable use cases for the dataset. -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
-
-## Dataset Structure
-
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-
-[More Information Needed]
-
-## Dataset Creation
-
-### Curation Rationale
-
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
 
-### Source Data
-
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-
-#### Data Collection and Processing
-
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-[More Information Needed]
-
-#### Who are the source data producers?
-
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-
-[More Information Needed]
-
-### Annotations [optional]
-
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-
-#### Annotation process
-
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
+## 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": "<image 0-25> After <move the grasped banana towards the mug>, what's the most probable next event?"
+            },
+            {
+                "from": "gpt",
+                "value": "<place the banana into the mug>"
+            }
+        ],
+        "image": [
+            "/path/to/image_0-25"
+        ]
+    }        
+}
+```
+
+     
+
+
+
+### Affordance
+
+<!--![2d94d985-d47e-4899-9760-c1cb8f19cd89](./images/2d94d985-d47e-4899-9760-c1cb8f19cd89.png)![a7817c0b-04b1-4a7c-9535-f9ff7801a689](./images/a7817c0b-04b1-4a7c-9535-f9ff7801a689.png)-->
+<div style="display: flex; gap: 10px;">
+  <img src="./images/2d94d985-d47e-4899-9760-c1cb8f19cd89.png" style="width: 300px;" />
+  <img src="./images/a7817c0b-04b1-4a7c-9535-f9ff7801a689.png" style="width: 300px;" />
+</div>
+
+```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)
+
+```
+
+
+
 
-#### Personal and Sensitive Information
 
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+### Trajectory
 
-[More Information Needed]
+<!-- ![5b923b31-dbbf-470f-af09-5125f5b91ab0](./images/5b923b31-dbbf-470f-af09-5125f5b91ab0.png)![1af4535a-acc3-4417-ae33-675f4301f560](./images/1af4535a-acc3-4417-ae33-675f4301f560.png)-->
+<div style="display: flex; gap: 10px;">
+  <img src="./images/5b923b31-dbbf-470f-af09-5125f5b91ab0.png" style="width: 300px;" />
+  <img src="./images/1af4535a-acc3-4417-ae33-675f4301f560.png" style="width: 300px;" />
+</div>
 
-## Bias, Risks, and Limitations
+```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
+            ]
+        ]
+    },
+```
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+#### Visualize Code
 
-[More Information Needed]
+```python
+import json
+import os
+from PIL import Image, ImageDraw
 
-### Recommendations
+trajectory_final = '/path/to/your/trajectory_json'
+img_dir = '/path/to/your/original/images/dir'
+output_img_dir = '/path/to/your/visualzed/images/dir'
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+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']
 
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+        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���颜色和线宽
+        color = (255, 0, 0)  # 红色 (RGB 格式)
+        thickness = 2
 
-## Citation [optional]
 
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+        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)
 
-**BibTeX:**
+        # 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)
 
-[More Information Needed]
+        # 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)
 
-**APA:**
+        # 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}")
 
-[More Information Needed]
+        # 保存图像
+        image.save(output_img_path)
+```
 
-## Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
 
-[More Information Needed]
+## Evaluation
 
-## More Information [optional]
 
-[More Information Needed]
 
-## Dataset Card Authors [optional]
 
-[More Information Needed]
 
-## Dataset Card Contact
+## Reference
 
-[More Information Needed]
+[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}
+}
+```