Update README.md

README.md

@@ -3421,4 +3421,51 @@ configs:
   data_files:
   - split: train
     path: unsolve01/train-*
+license: cc-by-4.0
+pretty_name: Puzzle Manipulation Datasets
 ---
+# Puzzle Manipulation Datasets
+
+<!-- Provide a quick summary of the dataset. -->
+
+This dataset is a collection of logged data from the teleoperation of [ergoCub](https://ergocub.eu/) manipulating a puzzle. 
+
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+This dataset contains data from the teleoperation of [ergoCub](https://ergocub.eu/) collected in an experiment where the robot first unsolves then solves a puzzle. 
+
+- **Curated by:** Giovanni Fregonese (@giotherobot)
+- **License:** CC BY 4.0
+
+## Dataset Creation
+
+#### Curation Rationale
+
+<!-- Motivation for the creation of this dataset. -->
+
+The dataset was created to enable the development of autonomous manipulation algorithms for the ergoCub robot.
+
+#### 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. -->
+
+The data colletion was performed using the tools developed for the ANA Avatar XPRIZE competition.
+The technology stack used for the data collection is described in the paper: [iCub3 avatar system: Enabling remote fully immersive embodiment of humanoid robots](https://www.science.org/doi/10.1126/scirobotics.adh3834).
+
+
+#### 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. -->
+
+The data was collected by the [AMI](https://ami.iit.it/) research line at the Istituto Italiano di Tecnologia.
+
+#### 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. -->
+
+The dataset does not contain personal or sensitive information.