Omkar2118/datasets huggingface dataset:
VAST Absorption

**VAST Absorption** is a dataset of spatial binaural features annotated with acoustic properties such as the 3D source position and the walls’ absorption coefficients.

Introducing paper: Hearing in a shoe-box : binaural source position and wall absorption estimation using virtually supervised learning
Bimanual Actions Dataset

The Bimanual Actions Dataset is a collection of 540 RGB-D videos, showing subjects perform bimanual actions in a kitchen or workshop context. The main purpose for its compilation is to research bimanual human behaviour in order to eventually improve the capabilities of humanoid robots.

Introducing paper: Learning Object-Action Relations from Bimanual Human Demonstration Using Graph Networks
ColosseumRL

**ColosseumRL** is a framework for research in reinforcement learning in n-player games.

ColosseumRL contains a number of multiagent free-for-all games. Currently, we have Tron, Blokus, and 3 and 4-player tic-tac-toe. In the future, we will be adding Chinese checkers and other similar games. Tron is a fully-observable multiagent free-for-all turn-based snake variant where players try to survive the longest without crashing into walls or each other. Blokus is a fully-observable multiagent free-for-all turn-based game in which players place pieces on a board to claim space and strategically block opponents from placing their own pieces.

Introducing paper: ColosseumRL: A Framework for Multiagent Reinforcement Learning in $N$-Player Games
Online Cryptocurrency-topic diffusion on Twitter, Telegram, and Discord

This Dataset is described in Charting the Landscape of Online Cryptocurrency Manipulation. IEEE Access (2020), a study that aims to map and assess the extent of cryptocurrency manipulations within and across the online ecosystems of Twitter, Telegram, and Discord. Starting from tweets mentioning cryptocurrencies, we leveraged and followed invite URLs from platform to platform, building the invite-link network, in order to study the invite link diffusion process.

Please, refer to the paper below for more details.

Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., Tesconi, M. & Ferrara, E. (2020). Charting the Landscape of Online Cryptocurrency Manipulation. IEEE Access (2020).

This dataset is composed of: 

~16M tweet ids shared between March and May 2019, mentioning at least one of the 3,822 cryptocurrencies (cashtags) provided by the CryptoCompare public API;
~13k nodes of the invite-link network, i.e., the information about the Telegram/Discord channels and Twitter users involved in the cryptocurrency discussion (e.g., id, name, audience, invite URL);
~62k edges of the invite-link network, i.e., the information about the flow of invites (e.g., source id, target id, weight).

Introducing paper: Charting the Landscape of Online Cryptocurrency Manipulation
3D-Printing-Data

This is a dataset for anomalies detection in 3D printing.

Introducing paper: Dataset for anomalies detection in 3D printing
Grasp Affordance

This is a dataset for visual grasp affordance prediction that promotes more robust and heterogeneous robotic grasping methods. The dataset contains different attributes from 30 different objects. Each object instance is related not only to the semantic descriptions, but also the physical features describing visual attributes, locations and different grasping regions related to a variety of actions.

Introducing paper: Learning Grasp Affordance Reasoning through Semantic Relations
Visual Servoing

Dataset for visual servoing (VS) and camera pose estimation. 
The images were obtained by a manipulator robot with an eye-in-hand camera in different poses. 
The labels represent the camera pose. 
It is possible to obtain the absolute pose of the camera without any pre-processing of the dataset, as well as the relative pose between images through matrix transformations. 
One may also use the dataset to get the camera's 6DoF speeds so that the visual servo control between two images can be performed.

Introducing paper: Real-Time Deep Learning Approach to Visual Servo Control and Grasp Detection for Autonomous Robotic Manipulation