LocoVR: Multiuser Indoor Locomotion Dataset in Virtual Reality
Authors: Kojiro Takeyama, Yimeng Liu, Misha Sra
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | 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. |