Reinforcement Learning

MuJoCo (multi-joint dynamics with contact) is a physics engine used to implement environments to benchmark Reinforcement Learning methods.

Omniverse Isaac Gym is a GPU-based physics simulation platform developed by NVIDIA. This open-source toolkit implements various Reinforcement Learning benchmarks, simulating real-world robotic applications. (Image Credit: NVIDIA-Omniverse)

The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician, eugenicist, and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species. Two of the three species were collected in the Gaspé Peninsula "all from the same pasture, and picked on the same day and measured at the same time by the same person with the same apparatus".

• This dataset was originally introduced by [1] for soccer ball and player tracking from six synchronized videos. - Since ball annotations provided by [1] are collapsed, new annotations of ball 2D coordinates are provided by [2] - For sports ball detection and tracking evaluation, the first four video clips are used for training and the remaining two clips are for testing. [1] T. D’Orazio et al., A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences, in AVSS, 2009. [2] S. Tarashima et al., Widely Applicable Strong Baseline for Sports Ball Detection and Tracking, in BMVC, 2023.

This dataset was introduced by [1], but was not used in its experiment. [2] propose to use all the clips included in the first 7 games as a training set, and the remainings as a testing set. [1] Y.-C. Huang et al., TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications. AVSS, 2019. [2] S. Tarashima et al., Widely Applicable Strong Baseline for Sports Ball Detection and Tracking, BMVC, 2023.

TBD

ATLANTIS is a benchmark for semantic segmentation of waterbody images. This dataset covers a wide range of natural waterbodies such as sea, lake, river and man-made (artificial) water-related structures such as dam, reservoir, canal, and pier. ATLANTIS includes 5,195 pixel-wise annotated images split to 3,364 training, 535 validation, and 1,296 testing images. In addition to 35 waterbodies, this dataset covers 21 general labels such as person, car, road and building.

HAMMER dataset contains 13 Scenes. Each scene has two setups, with/without objects (with : scene includes several objects with various surface material, without : scene with only backgrounds - naked) and each scene has two camera trajectories. Each trajectories composed with roughly 300 frames, which adds up to 16k frames in total (13 x 2 x 2 x 300). Each trajectory contains corresponding images from each cameras : d435 – stereo, l515 – Lidar (D-ToF), polarization – RGBP (RGB with polarization), tof – (I-ToF). Each camera folder contains its intrinsic file and its own recorded images together with rendered depth GT / instance GT and camera pose. All the cameras are fully synchronized via robotic arm’s data acquisition setup. Source: On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks Image Source: On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks

TruthGen is a dataset of generated true and false statements, intended for research on truthfulness in reward models and language models, specifically in contexts where political bias is undesirable. This dataset contains 1,987 statement pairs (3,974 statements in total), with each pair containing one objectively true statement and one false statement. It spans a variety of everyday and scientific facts, excluding politically charged topics to the greatest extent possible. The dataset is particularly useful for evaluating reward models trained for alignment with truth, as well as for research on mitigating political bias while improving model accuracy on truth-related tasks.