id
stringlengths 9
9
| video_path
stringlengths 19
19
| object_class
stringlengths 3
14
| grip_type
stringclasses 4
values | container_type
stringclasses 5
values | outcome
stringclasses 5
values | occlusion_frames
stringclasses 9
values | persistence
bool 2
classes |
|---|---|---|---|---|---|---|---|
clip_0001
|
data/train/0001.mp4
|
ceramic_mug
|
power
|
table
|
success
| null | true
|
clip_0002
|
data/train/0002.mp4
|
plastic_bottle
|
precision
|
table
|
slip
|
12-28
| true
|
clip_0003
|
data/train/0003.mp4
|
cardboard_box
|
pinch
|
conveyor
|
collision
|
7-15
| false
|
clip_0004
|
data/train/0004.mp4
|
metal_tool
|
lateral
|
shelf
|
success
| null | true
|
clip_0005
|
data/train/0005.mp4
|
glass_jar
|
power
|
table
|
drop
|
19-33
| true
|
clip_0006
|
data/train/0006.mp4
|
book
|
pinch
|
drawer
|
miss
|
4-9
| false
|
clip_0007
|
data/train/0007.mp4
|
ball
|
precision
|
free_space
|
success
| null | true
|
clip_0008
|
data/train/0008.mp4
|
ceramic_plate
|
power
|
table
|
slip
|
10-22
| true
|
clip_0009
|
data/train/0009.mp4
|
remote_control
|
lateral
|
shelf
|
collision
|
14-18
| false
|
clip_0010
|
data/train/0010.mp4
|
phone
|
pinch
|
table
|
success
| null | true
|
clip_0011
|
data/train/0011.mp4
|
stapler
|
power
|
conveyor
|
drop
|
21-35
| true
|
clip_0012
|
data/train/0012.mp4
|
pen
|
precision
|
drawer
|
miss
|
5-11
| false
|
clip_0013
|
data/train/0013.mp4
|
small_box
|
power
|
table
|
success
| null | true
|
clip_0014
|
data/train/0014.mp4
|
scissors
|
lateral
|
shelf
|
slip
|
9-16
| true
|
Overview
Short video clips of hand grips in rooms.
Each clip links grip, object, and container.
Designed to expose drift when space is treated as optional.
Why this matters
- grip changes when the room is known
- persistence matters when objects go off-frame
- mis-grips rise when boundaries are ignored
- current models track objects, not rooms
Data
- 14 example clips in v0.1
- mp4 videos, 2–8 seconds
- csv annotation (id, path, class, grip, container, outcome, occlusion, persistence)
Use cases
- robotics calibrations
- warehouse automation
- prosthetic training loops
- spatial benchmarks for world models
Suggested evaluations
- error rate before/after adding container features
- persistence score across occlusion
- boundary-aware grip outcome
- drift cost in mis-placement cycles
File structure
data/
train/
annotations.csv
README.md
Citation
Include link to this page if you publish results.
Contact
Pull request with improvements or new clips welcome.
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