YCB-Slide: A tactile interaction dataset

The YCB-Slide dataset comprises of DIGIT sliding interactions on YCB objects. We envision this can contribute towards efforts in tactile localization, mapping, object understanding, and learning dynamics models. We provide access to DIGIT images, sensor poses, RGB video feed, ground-truth mesh models, and ground-truth heightmaps + contact masks (simulation only). This dataset is supplementary to the MidasTouch paper, a CoRL 2022 submission.

Getting started: Dataset

The recommended way to download the YCB-Slide is through download_dataset.sh (before you do, please run pip install gdown). Or, you could download object-specific data here:

Getting started: Code

Setup the environment

conda env create -f environment.yml 
conda activate ycb_slide
pip install -e .

Download dataset

chmod +x download_dataset.sh   
./download_dataset.sh  # requires gdown

Or, you could download object-specific data here.

Visualize dataset

# Visualize trajectory
python visualize_trajectory.py --data_path dataset/real/035_power_drill/dataset_0 --object 035_power_drill --real # real dataset
python visualize_trajectory.py --data_path dataset/sim/035_power_drill/00 --object 035_power_drill # sim dataset

# Visualize tactile data
python visualize_tactile.py --data_path dataset/real/035_power_drill/dataset_0 --object 035_power_drill --real  # real dataset
python visualize_tactile.py --data_path dataset/sim/035_power_drill/00 --object 035_power_drill  # sim dataset

Record your own data

Plug in your DIGIT (and webcam optionally), and record your own trajectories:

python record_data_expt.py --duration 60 --webcam_port 2 --object 035_power_drill # record DIGIT images and webcam

You can also timesync and align your data with these scripts:

python timesync_digit.py --digit_file dataset/real/035_power_drill/dataset_0/digit_data.npy --tracking_file dataset/real/035_power_drill/035_power_drill.csv --bodies "DIGIT 035_power_drill" # timesync optitrack and DIGIT data

python align_data.py --data_path dataset/real/035_power_drill --object 035_power_drill # manually alignment fine-tuning

Dataset details

YCB objects: We select 10 YCB objects with diverse geometries in our tests: sugar_box, tomato_soup_can, mustard_bottle, bleach_cleanser, mug, power_drill, scissors, adjustable_wrench, hammer, and baseball. You can download the ground-truth meshes here.

Simulation: We simulate sliding across the objects using TACTO, mimicking realistic interaction sequences. The pose sequences are geodesic-paths of fixed length L = 0.5m, connecting random waypoints on the mesh. We corrupt sensor poses with zero-mean Gaussian noise $σ_\text{trans} = 0.5\text{mm}, \ σ_\text{rot} = 1^{\circ}$. We record five trajectories per-object, 50 interactions in total.

Real-world: We perform sliding experiments through handheld operation of the DIGIT. We keep each YCB object stationary with a heavy-duty bench vise, and slide along the surface and record 60 secs trajectories at 30Hz. We use an OptiTrack system for timesynced sensor poses (synced_data.npy), with 8 cameras tracking the reflective markers. We affix six markers on the DIGIT and four on each test object. The canonical object pose is adjusted to agree with the ground-truth mesh models. Minor misalignment of sensor poses are rectified by postprocessing (alignment.npy). We record five logs per-object, for a total of 50 sliding interactions

Representative experiments	All 50 trajectories

Object	Sim data	Real data	Object	Sim data	Real data
004_sugar_box	[100.6 MB]	[321.9 MB]	035_power_drill	[111.3 MB]	[339.1 MB]
005_tomato_soup_can	[147.9 MB ]	[327.4 MB]	037_scissors	[109 MB]	[320 MB]
006_mustard_bottle	[102.7 MB]	[319.4 MB]	042_adjustable_wrench	[147.7 MB]	[326.9 MB]
021_bleach_cleanser	[86.5 MB]	[318.5 MB]	048_hammer	[102 MB]	[320.9 MB]
025_mug	[107 MB]	[325.5 MB]	055_baseball	[153.4 MB]	[331.1 MB]

Data directory format

Simulation

sim
├── object_0 # eg: 004_sugar_box
│   ├── 00
│   │   ├── gt_contactmasks # ground-truth contact masks
│   │   │   ├── 0.jpg
│   │   │   └── ...
│   │   ├── gt_heightmaps # ground-truth depth maps
│   │   │   ├── 0.jpg
│   │   │   └── ...
│   │   ├── tactile_images # RGB DIGIT images
│   │   │   ├── 0.jpg
│   │   │   └── ...
│   │   ├── tactile_data_noisy.png # 3-D viz w/ noisy poses
│   │   ├── tactile_data.png # 3-D viz
│   │   └──tactile_data.pkl # pose information
│   ├── 01
│   └── ...
├── object_1
└── ...

Real-world

real
├── object_0 # eg: 004_sugar_box
│   ├── dataset_0
│   │   ├── frames # RGB DIGIT images
│   │   │   ├── frame_0000000.jpg
│   │   │   ├── frame_0000001.jpg
│   │   │   └── ...
│   │   ├── webcam_frames # RGB webcam images
│   │   │   ├── frame_0000000.jpg
│   │   │   ├── frame_0000001.jpg
│   │   │   └── ...
│   │   ├── digit_data.npy # pose information
│   │   ├── frames.mp4 # RGB DIGIT video
│   │   ├── synced_data.npy # timesynced image/pose data
│   │   ├── tactile_data.png # 3-D viz
│   │   └── 3d_rigid_bodies.pdf # pose viz
│   ├── object_0.csv # raw optitrack data
│   ├── alignment.npy # adjustment tf matrix
│   └── tactile_data.png # 3-D viz
│   ├── dataset_1
│   └── ...
├── object_1
└── ...

License

This dataset is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License, with the accompanying code licensed under the MIT License.

Citation

If you use YCB-Slide dataset in your research, please cite:

@inproceedings{suresh2022midastouch,
    title={{M}idas{T}ouch: {M}onte-{C}arlo inference over distributions across sliding touch},
    author={Suresh, Sudharshan and Si, Zilin and Anderson, Stuart and Kaess, Michael and Mukadam, Mustafa},
    booktitle = {Proc. Conf. on Robot Learning, CoRL},
    address = {Auckland, NZ},
    month = dec,
    year = {2022}
}

rpl-cmu / YCB-Slide