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README.md

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-# Dataset Card for RoAM
+# About RoAM
 
 We introduce Robot Autonomous Motion (RoAM), a unique video-action dataset that includes 50 long video sequences collected over 7 days at 14 different indoor spaces, capturing various indoor human activities from the ego-motion perspective of the Turtlebot3 robot. Along with the stereo image sequences, RoAM also contains time-stamped robot action sequences that are synchronised with the video data. The dataset primarily includes a range of human movements, such as walking, jogging, hand-waving, gestures, and sitting actions, which an indoor robot might encounter while navigating environments populated by people.
 Each of the 50 recorded video sequences, is started with unique initial conditions such that there is sufficient diversity and variations in the dataset. The dataset pre-dominantly records human walking motion while the robot slowly explores its surroundings.
 
-## Dataset Details
 
 ### Dataset Description
 
 The RoAM dataset is collected using a custom-built Turtlebot3 Burger robot. We have used the Tensorflow Dataset API to generate 3,07,200 video-action sequences of length 25 for training our variational and diffusion models. It also contains the corresponding action values from the robot's motion to capture the movement of the camera. We have used Zed mini stereo vision camera for capturing the left and right timestamped image pairs. Other than that the robot is equipped with an LDS-01 2-dimensional LiDAR, a TP-link WiFi communication module as shown in Figure above. The Turtlebot3 employs two DYNAMIXEL XL430-W250 servo motors for navigation, utilizing current-based torque control. These motors are actuated and controlled by the OpenCR-01 board, which is integrated into the platform. For our specific application, we have selected the Jetson TX2 board as the onboard computer, operating on the ROS Melodic framework and the Ubuntu 18.04 operating system. This setup offers the advantage of leveraging the Jetson TX2's high computational power to support complex robotic tasks, such as perception, navigation, and machine learning.
 
 
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
+- **Curated by:** [Meenakshi Sarkar ]
 
-### Dataset Sources [optional]
 
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [https://github.com/meenakshisarkar/RoAM-dataset.git]
 
 ## 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]
+Video Generation and Prediction, Robot motion, Camera Action, Reinforcement Learning
 
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
+### Caution
 
+Dataset Purpose: Research in video generative models
+Potential Misuse: Risk of exploitation for creating and spreading misinformation through deepfake videos
 ## Dataset Structure
+dataset is currently available in TFRecord file format.
+## TFRecord File 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]
-
-#### 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. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+This dataset is stored in TFRecord format and contains the following features:
 
-[More Information Needed]
+### Features
 
-### Recommendations
+1. **image_left**
+   - Shape: [256, 256, 4]
+   - Data type: uint8
+   - Description: Image data, with 4 channels (e.g., RGBA)
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+2. **action**
+   - Shape: [1, 2]
+   - Data type: float32
+   - Description: Represents an robot/camera action or movement vector
 
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+3. **folder_name**
+   - Shape: [1]
+   - Data type: string
+   - Description: Name of the folder associated with the date and the location of the indoor place where the data was collected
 
-## Citation [optional]
+### Additional Information
 
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+- **Partition**: test
+- **Number of files**: 5
+- **Partition**: train
+- **Number of files**: 45
 
-**BibTeX:**
 
-[More Information Needed]
 
-**APA:**
 
-[More Information Needed]
 
-## Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
 
-[More Information Needed]
+## Citation 
 
-## More Information [optional]
+@InProceedings{acpnet2023,
+  author={Sarkar, Meenakshi and Honkote, Vinayak and Das, Dibyendu and Ghose, Debasish},
+  booktitle={2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)}, 
+  title={Action-conditioned Deep Visual Prediction with RoAM, a new Indoor Human Motion Dataset for Autonomous Robots}, 
+  year={2023},
+  volume={},
+  number={},
+  pages={1115-1120},
+  doi={10.1109/RO-MAN57019.2023.10309423}
+}
 
-[More Information Needed]
 
-## Dataset Card Authors [optional]
+## Dataset Card Authors 
 
-[More Information Needed]
+[Meenakshi Sarkar]
 
 ## Dataset Card Contact
 
-[More Information Needed]
+[[email protected]]