Find research datasets worth reusing

BISCUIT: Causal Representation Learning from Binary Interactions

<p>This repository contains the datasets from the paper &quot;BISCUIT: Causal Representation Learning from Binary Interactions&quot;&nbsp;(<a href="https://phlippe.github.io/BISCUIT/">link</a>) by Phillip Lippe,&nbsp;Sara Magliacane,&nbsp;Sindy L&ouml;we,&nbsp;Yuki M. Asano,&nbsp;Taco Cohen,&nbsp;Efstratios Gavves.&nbsp;</p> <p><strong>iTHOR Embodied AI&nbsp;</strong>-&nbsp;The Embodied AI dataset is generated with the iTHOR simulator. We use the default kitchen environment, FloorPlan10, and position the robot in front of the kitchen counter. The robot interacts with different objects in the environment, including a Microwave, cabinet, stove, and an egg. For more details on the dataset, see&nbsp;<a href="https://github.com/phlippe/BISCUIT">our GitHub repository</a>&nbsp;and the appendix of our&nbsp;paper.</p> <p><strong>CausalWorld&nbsp;</strong>- The CausalWorld environment implements a tri-finger robot which can interact with a cube in the center of a stage. We additionally introduce causal variables for the friction parameters of the stage, floor and cube, as well as color changes.&nbsp;For more details on the dataset as well as the code to generate this dataset, see&nbsp;<a href="https://github.com/phlippe/BISCUIT">our GitHub repository</a>&nbsp;and the appendix of our paper.</p> <p><strong>Voronoi</strong> - The Voronoi benchmark allows for creating causal systems with arbitrary number of causal variables and causal graphs. We provide datasets with 6 and 9 variables, as well as systems with minimal number of interactions. For details, see&nbsp;<a href="https://github.com/phlippe/BISCUIT">our GitHub repository</a>&nbsp;and our paper.</p>