pretty_name: ManiSoft
license: mit
language:
- en
size_categories:
- 1K<n<10K
task_categories:
- robotics
tags:
- robotics
- soft-robotics
- manipulation
- imitation-learning
- vision-language-action
- embodied-ai
- simulation
ManiSoft
ManiSoft is a soft-robot manipulation dataset and benchmark for vision-language-action learning. It contains expert demonstrations for four manipulation tasks:
COLL: CollectionALN: AlignmentARR: ArrangementSTK: Stacking
This upload directory currently provides:
assets.tar: simulator assets required for replay and trainingclean/: task data packaged as.tarshards for efficient download and uploaddata_extract.sh: a utility script for recursively extracting all dataset shards
Task Layout in This Repository
The files hosted in the dataset repository are organized as tar shards rather than already-extracted case folders.
.
├── assets.tar
├── clean
│ ├── ALN
│ │ ├── train_bottle_0_9.tar
│ │ ├── train_bottle_10_19.tar
│ │ ├── eval_bottle_0_9.tar
│ │ └── ...
│ ├── ARR
│ │ ├── eval_bottle_0_9.tar
│ │ └── ...
│ ├── COLL
│ │ ├── train_pencup_0_9.tar
│ │ ├── eval_boxdrink_0_9.tar
│ │ └── ...
│ └── STK
│ ├── train_default_0_9.tar
│ ├── eval_default_0_9.tar
│ └── ...
└── data_extract.sh
For ALN, ARR, and COLL, shard names follow:
<split>_<object_category>_<start_case_id>_<end_case_id>.tar
For STK, shard names follow:
<split>_default_<start_case_id>_<end_case_id>.tar
Extracted Dataset Format
After extraction, each shard restores the original directory structure. A typical case directory looks like this:
clean/
└── ALN/
├── train/
│ └── bottle/
│ └── 0/
│ ├── environment.yaml
│ ├── instructions.txt
│ ├── trajectory.pkl
│ └── visual/
└── eval/
└── bottle/
└── 0/
├── environment.yaml
├── instructions.txt
├── trajectory.pkl
└── visual/
Each case is typically organized by:
<setting>/<task>/<split>/<object_category>/<case_id>/
Common files inside one case:
instructions.txt: language instructions for the manipulation caseenvironment.yaml: scene and task configurationtrajectory.pkl: expert trajectory stored as a time-indexed dictionaryvisual/: visualization assets such as rendered frames or videos
Quick Download Example
If you use the Hugging Face CLI, you can download the dataset to a local directory like this:
hf download JobsWei/ManiSoft --local-dir ./ManiSoft --repo-type dataset
If you only need the benchmark data without simulator assets:
hf download JobsWei/ManiSoft --local-dir ./ManiSoft --repo-type dataset --exclude "assets.tar"
If you only need evaluation shards:
hf download JobsWei/ManiSoft --local-dir ./ManiSoft --repo-type dataset --include "**/eval/**"
data_extract.sh Usage
The repository includes data_extract.sh for recursively finding and extracting all .tar files under a root directory with parallel workers.
Command
bash data_extract.sh <tar_root_dir> <max_processes> <delete_tar_file>
Arguments
tar_root_dir: root directory to recursively search for.tarfilesmax_processes: number of parallel extraction processes, must be a positive integerdelete_tar_file: whether to delete each.tarafter successful extraction0: keep tar files1: delete tar files
Typical Examples
Extract all dataset shards under the downloaded directory and keep the original tar files:
bash data_extract.sh ./ManiSoft 8 0
Extract all dataset shards and delete each tar file after successful extraction:
bash data_extract.sh ./ManiSoft 8 1
Extract only the clean subset:
bash data_extract.sh ./ManiSoft/clean 8 1
What the Script Does
- recursively finds all
.tarfiles undertar_root_dir - extracts them in parallel
- restores files into the original relative paths stored in each tar shard
- optionally removes the source tar files after successful extraction
Recommended Workflow
hf download JobsWei/ManiSoft --local-dir ./ManiSoft --repo-type dataset --exclude "assets.tar"
cp /path/to/data_extract.sh ./ManiSoft/
cd ./ManiSoft
bash data_extract.sh ./clean 8 1
If you also need simulator assets:
tar -xvf assets.tar
Notes
- The extraction script requires a Unix-like shell environment with
bash,find,tar, and standard job control support. - Different shards may expand into the same
train/oreval/directory tree. This is expected. trajectory.pklis the main expert trajectory file used for imitation learning and replay.