| <center><h1>2026 ICLR Submission</h1> |
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| Current vision-language models (VLMs) struggle with long-term memory in embodied tasks. To address this, we introduce **FindingDory**, a benchmark in Habitat that evaluates memory-based reasoning across 60 long-horizon tasks. |
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| In this repo, we release the FindingDory Habitat Dataset. The episode dataset is used to run online evaluations with the Habitat simulator using VLM-based navigation agents. Each episode involves a robot performing multiple pick-place object interactions. The agent then needs to execute actions in the simulator complete the tasks specified in the FindingDory benchmark. |
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| We also release datasets that can be used for supervised finetuning of multimodal LLMs using the video-task pairs in FindingDory |
| * Full video length dataset [(here)](https://huggingface.co/datasets/findingdory/findingdory-video-sft) |
| * 96-frame subsampled dataset [(here)](https://huggingface.co/datasets/findingdory/findingdory-video-sft-96) |
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| # Habitat Dataset Structure |
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| This repository contains data and models for the **findingdory-habitat** package, structured into two main components: **findingdory** and **imgnav**. |
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| ## **findingdory** |
| | File/Folder | Description | |
| | ------------------------------ | --------------------------------------------------------------------------- | |
| | **train/** | Training split of the habitat dataset. | |
| | ├── `episodes.json.gz` | Encoded JSON file containing episode metadata for training episodes. | |
| | ├── `transformations.npy` | NumPy array of transformation matrices (of each rigid object in simulator). | |
| | └── `viewpoints.npy` | NumPy array of viewpoints (camera poses of all objects/receptacles). | |
| | **val/** | Validation split of the habitat dataset. | |
| | ├── `episodes.json.gz` | Encoded JSON file containing episode metadata for validation episodes. | |
| | ├── `transformations.npy` | NumPy array of transformation matrices (of each rigid object in simulator). | |
| | └── `viewpoints.npy` | NumPy array of viewpoints (camera poses of all objects/receptacles). | |
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| ## **findingdory_imagenav** |
| | File/Folder | Description | |
| | ----------------------------------- | -------------------------------------------------------------------------------------- | |
| | **dataset/** | Image navigation dataset (split into train and val). | |
| | ├── `train/` | Training data for the image navigation task. | |
| | └── `val/` | Validation data for the image navigation task. | |
| | **policy_ckpt/** | Policy checkpoint files (trained navigation policies). | |
| | └── `ckpt.17.pth` | PyTorch checkpoint file containing trained policy weights. | |
| | **pretrained_vis_enc/** | Pretrained visual encoder weights. | |
| | └── `ckpt.16.pth` | PyTorch checkpoint file for the pretrained visual encoder used in imagenav training. | |
