Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
๐๏ธ ProcTHOR: Large-Scale Embodied AI Using Procedural Generation
Authors: Matt Deitke, Eli VanderBilt, Alvaro Herrasti, Luca Weihs, Kiana Ehsani, Jordi Salvador, Winson Han, Eric Kolve, Aniruddha Kembhavi, Roozbeh Mottaghi
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the power and potential of PROCTHOR via a sample of 10,000 generated houses and a simple neural model. Models trained using only RGB images on PROCTHOR, with no explicit mapping and no human task supervision produce state-of-the-art results across 6 embodied AI benchmarks for navigation, rearrangement, and arm manipulation... |
| Researcher Affiliation | Collaboration | PRIOR @ Allen Institute for AI, ฯUniversity of Washington, Seattle |
| Pseudocode | No | The paper describes the procedural generation process through text and diagrams (e.g., Figure 2), but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | PROCTHOR will be open-sourced and the code used in this work will be released. |
| Open Datasets | Yes | We demonstrate the power and potential of PROCTHOR using a sampled set of 10,000 fully interactive houses obtained by the procedural generation process described in Section 3 which we label PROCTHOR-10K. An additional set of 1,000 validation and 1,000 testing houses are available for evaluation. |
| Dataset Splits | Yes | An additional set of 1,000 validation and 1,000 testing houses are available for evaluation. Asset splits across train/val/test are detailed in the Appendix. |
| Hardware Specification | Yes | This set of 10K houses was generated in 1 hour on a local workstation with 4 NVIDIA RTX A5000 GPUs. Experiments were run on a server with 8 NVIDIA Quadro RTX 8000 GPUs. |
| Software Dependencies | No | The paper lists numerous open-source packages used (e.g., 'Py Torch [41]', 'Num Py [23]', 'Tensor Flow [1]') but does not provide specific version numbers for these software dependencies, only citations to their original papers or general project pages. |
| Experiment Setup | Yes | All models are trained with the Allen Act [56] framework, see the Appendix for training details. |