{"url":"/dataset/igibson-2-0","name":"iGibson 2.0","full_name":null,"description_markdown":"**iGibson 2.0** is an open-source simulation environment that supports the simulation of a more diverse set of household tasks through three key innovations. First, iGibson 2.0 supports object states, including temperature, wetness level, cleanliness level, and toggled and sliced states, necessary to cover a wider range of tasks. Second, iGibson 2.0 implements a set of predicate logic functions that map the simulator states to logic states like Cooked or Soaked. Additionally, given a logic state, iGibson 2.0 can sample valid physical states that satisfy it. This functionality can generate potentially infinite instances of tasks with minimal effort from the users. The sampling mechanism allows our scenes to be more densely populated with small objects in semantically meaningful locations. Third, iGibson 2.0 includes a virtual reality (VR) interface to immerse humans in its scenes to collect demonstrations.","description_withheld":null,"homepage":"http://svl.stanford.edu/igibson/","introduced_date":"2021-08-06","introduced_date_note":null,"introduced_by":{"paper":"/paper/igibson-2-0-object-centric-simulation-for","title":"iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks","first_author":"Chengshu Li","url":null},"license":null,"modalities":[{"name":"Environment","url":"/datasets/modality/environment"}],"tasks":[],"languages":[],"variants":["iGibson 2.0"],"data_loaders":[],"num_papers_in_archive":10,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}