Search is not available for this dataset
observation.state listlengths 8 8 | observation.states.ee_state listlengths 6 6 | observation.states.joint_state listlengths 7 7 | observation.states.gripper_state listlengths 2 2 | action listlengths 7 7 | timestamp float32 0 25.2 | frame_index int64 0 504 | episode_index int64 0 449 | index int64 0 95.7k | task_index int64 0 9 | observation.xvla_abs_ee6d listlengths 10 10 | actino.xvla_abs_ee6d listlengths 10 10 |
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End of preview. Expand in Data Studio
MiniCPM-RobotManip LIBERO
This dataset contains the four LIBERO suites converted to LeRobot v3 format for the MiniCPM-RobotManip LIBERO full-parameter fine-tuning example in starVLA.
Dataset summary
| Suite | Episodes | Frames | Videos |
|---|---|---|---|
| LIBERO-10 | 358 | 95,740 | 716 |
| LIBERO-Goal | 405 | 48,131 | 810 |
| LIBERO-Object | 450 | 66,294 | 900 |
| LIBERO-Spatial | 423 | 51,707 | 846 |
| Total | 1,636 | 261,872 | 3,272 |
- Format: LeRobot v3
- Frequency: 20 Hz
- Cameras: agent view and wrist view
- Video resolution: 256 × 256
- Action target:
observation.xvla_abs_ee6d - Action target dimension: 10
EE6D target
observation.xvla_abs_ee6d contains an absolute single-arm target:
[0:3]: xyz position[3:9]: rotation6D[9]: gripper closed (0= open,1= closed)
The starVLA MiniCPM-RobotManip recipe uses the current row as state and offsets
1–30 as the action chunk. It maps this 10-D vector to the left-arm [7:17]
slot of MiniCPM-RobotManip's unified 80-D state/action space; other channels are
masked out of the training loss.
Each suite includes meta/modality.json with the mapping expected by starVLA.
Legacy auxiliary column
The parquet files retain actino.xvla_abs_ee6d, a legacy auxiliary field from
the original conversion pipeline. It is intentionally preserved for provenance
and is not consumed by the published starVLA recipe. Use
observation.xvla_abs_ee6d for both state and offset action targets.
Download
hf download openbmb/MiniCPM-RobotManip-LIBERO \
--repo-type dataset \
--local-dir playground/Datasets/LIBERO_EE6D
Expected top-level directories:
libero_10_agentview_rot180_wrist_raw_lerobot_v30_xvla_abs6d/
libero_goal_agentview_rot180_wrist_raw_lerobot_v30_xvla_abs6d/
libero_object_agentview_rot180_wrist_raw_lerobot_v30_xvla_abs6d/
libero_spatial_agentview_rot180_wrist_raw_lerobot_v30_xvla_abs6d/
Training with starVLA
VLM_PATH=openbmb/MiniCPM-RobotManip \
LIBERO_EE6D_ROOT=playground/Datasets/LIBERO_EE6D \
bash examples/modelExtensions/MiniCPM-RobotManip/train_files/run_libero_train.sh
The corresponding public base checkpoint is
openbmb/MiniCPM-RobotManip.
Source and processing
The demonstrations originate from the LIBERO benchmark. The four suites were filtered, converted to LeRobot v3 at 20 Hz, and augmented with absolute EE6D targets aligned to each parquet row and video frame. Original observation and action fields are retained.
This release contains simulation demonstrations only. It does not contain personal or human-subject data.
License
Released under the Apache License 2.0. See LICENSE.
Citation
Please cite LIBERO and MiniCPM-Robot when using this dataset:
@inproceedings{liu2023libero,
title={LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning},
author={Liu, Bo and Zhu, Yifeng and Gao, Chongkai and Feng, Yihao and Liu, Qiang and Zhu, Yuke and Stone, Peter},
booktitle={Advances in Neural Information Processing Systems},
year={2023}
}
For the current MiniCPM-Robot citation, see the MiniCPM-Robot project.
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