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..
LocoVR: Multiuser Indoor Locomotion Dataset in Virtual Reality
Authors: Kojiro Takeyama, Yimeng Liu, Misha Sra
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our evaluation shows that Loco VR significantly enhances model performance in three practical indoor tasks utilizing human trajectories, and demonstrates predicting socially-aware navigation patterns in home environments. |
| Researcher Affiliation | Collaboration | Kojiro Takeyama1,2, Yimeng Liu1, Misha Sra1 1: University of California Santa Barbara, 2: Toyota Motor North America EMAIL |
| Pseudocode | No | The paper describes model architectures, inputs, outputs, and loss functions in detail in Section B 'Experimental Details', but it does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The dataset and evaluation code are available at https://github.com/kt2024-hal/Loco VR. |
| Open Datasets | Yes | The dataset and evaluation code are available at https://github.com/kt2024-hal/Loco VR. |
| Dataset Splits | Yes | Loco VR: Loco VR is our main contribution, and it was collected using our VR system. The dataset includes over 7000 trajectories in 131 indoor environments. We split it into training (85%) and validation sets (15%). |
| Hardware Specification | Yes | Each model is trained for up to 100 epochs on a single NVIDIA RTX 4080 graphics card with 8G memory. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and U-Net models, but does not provide specific version numbers for these or other key software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We use the Adam optimizer (Kingma & Ba, 2014) to train the U-Net models used in the experiments. The learning rate is 5e-5, and the batch size is 16. Each model is trained for up to 100 epochs on a single NVIDIA RTX 4080 graphics card with 8G memory. |