GVLA/MiGA-Dataset

MiGA: Multi-gripper dataset for Gripper-aware Vision-Language-Action models

Companion dataset for GVLA (Gripper-aware Vision-Language-Action Models).

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Overview of MiGA

MiGA is a large-scale multi-gripper-aware dataset featuring diverse gripper types on complex tasks, explicitly capturing how the same task requires different strategies depending on the gripper morphology.

• Format: LeRobot dataset schema, stored as Parquet
• Domains: real-world + simulation
• Modalities: third-person RGB (image), wrist-camera RGB (wrist_image), robot state (pose + gripper opening, 8-dim), actions (delta pose + gripper command, 7-dim), per-episode/task indices
• License: Apache 2.0

Composition of MiGA

MiGA is released as a large-scale multi-gripper-aware dataset featuring diverse gripper types on complex tasks, explicitly capturing how the same task requires different strategies depending on the gripper morphology. MiGA is built on five different gripper types (parallel-jaw, vacuum/suction, 3-finger, dexterous hand, soft gripper). Subsets are grouped below by domain (real-world vs. simulation).

Real-world subsets

Gripper type	Gripper config	Robot platform	Episodes	Format	HF dataset repo
Parallel-jaw	Franka panda hand	Franka Panda	1877	Parquet (LeRobot)	GVLA/Franka_panda_parallel_hand_real
Vacuum	Cobot Pump	Franka + Cobot	671	Parquet (LeRobot)	GVLA/Franka_cobot_vacuum_real
Dexterous hand	Inspire dexterous hand	Franka + Inspire Hand	899	Parquet (LeRobot)	GVLA/Franka_inspire_hand_real
Parallel-jaw	Robotiq 85 parallel-jaw	UR5	106	Parquet (LeRobot)	GVLA/UR5_robotiq_85_parallel_real
3 finger	Robotiq 3-f	UR5	-	Parquet (LeRobot)	GVLA/UR5_robotiq_3f_3finger_real
Soft Gripper	Soft-fin	X-arm7	-	Parquet (LeRobot)	coming soon
Simulation subsets

Gripper type	Gripper config	Robot platform	Episodes	Format	HF dataset repo
Parallel-jaw	Franka panda hand	Franka Panda	-	Parquet (LeRobot)	GVLA/Franka_panda_parallel_hand_sim
Parallel-jaw	Robotiq 85 parallel-jaw	Franka Panda	4065	Parquet (LeRobot)	GVLA/Franka_robotiq_85_parallel_sim
Vacuum	Cobot	Franka Panda	4829	Parquet (LeRobot)	GVLA/Franka_cobot_vacuum_sim
Vacuum	Short-cup vacuum	Franka Panda	-	Parquet (LeRobot)	GVLA/Franka_short_cup_vacuum_sim
Vacuum	Short-cup vacuum	UR10	2949	Parquet (LeRobot)	GVLA/UR10_short_cup_vacuum_sim

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Data description

Each subset follows the standard LeRobot dataset layout (Parquet data files + meta/info.json), auto-converted by Hugging Face into a browsable Parquet dataset:

GVLA/<gripper_config_repo>/
|-- data/
|   `-- train-*.parquet        # episodes concatenated, indexed by episode_index/frame_index
|-- videos/                    # camera MP4s (if published separately from the parquet)
|-- meta/
|   `-- info.json              # robot type, fps, feature schema

Per-frame fields (confirmed from the live dataset viewer)

Field	Type	Description
image	image (224×224)	Third-person RGB frame
wrist_image	image (224×224)	Wrist-camera RGB frame
state	float32[8]	End-effector pose (xyz + rotation) + gripper opening (2 values)
actions	float32[7]	Delta end-effector action + gripper command
gripper_id	int32	Identifies which gripper config generated this episode (e.g. 0 = parallel-jaw sim, 2 = parallel-jaw real)
timestamp	float32	Time within episode (s)
frame_index	int64	Frame index within episode
episode_index	int64	Episode index within subset
index	int64	Global row index
task_index	int64	Task identifier

Example of data usage

from datasets import load_dataset

# Load one gripper-config subset directly
ds = load_dataset("GVLA/Franka_panda_parallel_hand_real", split="train")
print(ds[0]["gripper_id"], ds[0]["state"], ds[0]["actions"])

# Or with the LeRobot dataset loader (recommended for training)
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("GVLA/Franka_panda_parallel_hand_real")

To combine subsets for cross-gripper training, concatenate on the shared state/actions/gripper_id schema and use gripper_id to condition or stratify by embodiment.

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Version update

Version 1.0

Initial release across seven gripper/domain subsets: Franka parallel-jaw (sim + real), Franka+Cobot vacuum (real), Franka+Inspire hand (real), UR5+Robotiq 85 parallel-jaw (real), UR5+Robotiq 3-finger (real), and UR10+short-cup vacuum (sim).

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Citation

If you find MiGA useful in your research, please cite:

@inproceedings{zhang2026gvla,
  title     = {Gripper-aware Vision Language Action Models},
  author    = {Hanyi Zhang, Zihong Luo, Tianyu Li, Khang Nguyen, Basu Hela, Shreyas Kumar, Ngoc Duy Tran, Feng Dai, Charith Munasinghe, Jorge Peña Queralta, Giovanni Toffetti, Khoa Vo, Ngan Le, Ravi Prakash, Quan Vuong, Tung D. Ta, Long Hu, Anh Nguyen, and Baoru Huang},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2026},
  eprint    = {2608.24603},
  archivePrefix = {arXiv}
}

Paper: arxiv.org/html/2608.24603v1

Reference

• Project page: [https://airvlab.github.io/G-VLA/]