ahmedsohail2003/smolvla-so101-pickplace
Robotics • 0.5B • Updated • 63
Datasets:
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observation.state list | action list | timestamp float32 0 7.33 ⌀ | frame_index int64 0 110 ⌀ | episode_index int64 0 99 ⌀ | index int64 0 11.1k ⌀ | task_index int64 0 0 ⌀ |
|---|---|---|---|---|---|---|
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1.284040093421936,
-1.7916451692581177,
1.4587277173995972
] | [
-1.1060141324996948,
-1.4765204191207886,
1.7644544839859009,
1.2820653915405273,
-1.7934948205947876,
1.399999976158142
] | 6.4 | 96 | 0 | 96 | 0 |
[
-1.104419469833374,
-1.468726396560669,
1.769166350364685,
1.283974289894104,
-1.791705846786499,
1.4317383766174316
] | [
-1.1060141324996948,
-1.4765204191207886,
1.7644544839859009,
1.2820653915405273,
-1.7934948205947876,
1.399999976158142
] | 6.466667 | 97 | 0 | 97 | 0 |
[
-1.1044596433639526,
-1.468679428100586,
1.7692270278930664,
1.2839105129241943,
-1.7917646169662476,
1.4127206802368164
] | [
-1.1060141324996948,
-1.4765204191207886,
1.7644544839859009,
1.2820653915405273,
-1.7934948205947876,
1.399999976158142
] | 6.533333 | 98 | 0 | 98 | 0 |
[
-1.1044988632202148,
-1.4686338901519775,
1.769285798072815,
1.2838488817214966,
-1.7918213605880737,
1.4053980112075806
] | [
-1.1059600114822388,
-1.4780385494232178,
1.7259635925292969,
1.313559889793396,
-1.7756403684616089,
1.399999976158142
] | 6.6 | 99 | 0 | 99 | 0 |
End of preview. Expand in Data Studio
so101-sim-pickplace
100 episodes / 11,100 frames of successful SO-ARM100 pick-and-place demonstrations in MuJoCo, 15 fps, two RGB cameras.
Part of Sim2Cell, a from-scratch imitation-learning pipeline on a simulated industrial work-cell: a MuJoCo SO-ARM100 arm (5-DoF + jaw) picks a block off the floor and places it in a tray, recorded straight to LeRobotDataset v3 and used to train ACT from pixels.
Task (language-labeled): "Pick up the red block and place it in the blue tray."
This is the nominal set: 100 successful expert episodes in the unperturbed scene. It is the baseline-training half of a measured robustness study (see trilogy table below).
Format
| Codebase | LeRobotDataset v3.0 (LeRobot 0.6.0) |
| FPS | 15 |
observation.state |
6-dim joint positions: Rotation, Pitch, Elbow, Wrist_Pitch, Wrist_Roll, Jaw |
action |
6-dim joint-position setpoints (same order) |
observation.images.front |
224x224 RGB video (AV1), fixed front camera |
observation.images.wrist |
224x224 RGB video (AV1), wrist-mounted camera |
Usage
from lerobot.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("ahmedsohail2003/so101-sim-pickplace")
# On Windows, add: video_backend="pyav"
Collection
Episodes are generated by a scripted 8-phase expert (damped-least-squares IK with a gripper-down constraint, block-yaw-aligned grasps) running headless in MuJoCo 3.10. Only episodes that end with the block verifiably inside the tray are saved -- failures are discarded at record time, so every episode is a successful demonstration.
The Sim2Cell dataset trilogy
| Dataset | Episodes | Purpose |
|---|---|---|
| so101-sim-pickplace | 100 | Nominal scene -- baseline ACT training |
| so101-sim-pickplace-v2 | 160 | Adds recovery demonstrations (perturbed first grasp -> verified retry) |
| so101-sim-pickplace-dr | 150 | Domain-randomized -- visual + physics variation for robustness training |
Measured motivation for the trilogy (15 eval episodes per randomization level,
identical protocol): an ACT policy trained on the nominal set reaches 13/20
(65%) from pixels (75% with temporal ensembling) but degrades under domain
randomization -- 60% nominal down to 40% at full DR -- while the privileged
scripted expert stays at 100% everywhere. Training the identical ACT recipe on
the -dr set closes the gap and then some: 87-100% across every
randomization level and 18/20 (90%) on nominal
(act-so101-pickplace-dr).
An earlier version of this paragraph claimed a 0% collapse for the nominal
policy; that number was a measurement artifact (an eval-harness rendering bug
fed the policy black frames) and is corrected here.
Provenance & license
Robot model vendored from MuJoCo Menagerie
(trs_so_arm100, Apache-2.0), with TCP-site and wrist-camera patches.
Dataset license: Apache-2.0.
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