hello3x3/libero_object_no_noops_1.0.0_lerobot_v3.0

libero_object_no_noops_1.0.0_lerobot

Dataset Description

This dataset is the LIBERO-Object subset of the LIBERO benchmark, converted to LeRobot v3.0 format.

10 object generalization tasks. All tasks share the same spatial layout but involve different objects, testing the agent's ability to generalize to novel object instances.

Note: This dataset has been filtered to remove no-op frames (idle frames where the robot does not execute meaningful actions). This results in more efficient training data.

Source

• Original benchmark: LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning (Liu et al., 2023)
• Source dataset: IPEC-COMMUNITY/libero_object_no_noops_1.0.0_lerobot by IPEC-COMMUNITY
• Conversion: v2.1 -> v3.0 using lerobot/datasets/v30/convert_dataset_v21_to_v30.py

Dataset Statistics

Property	Value
Robot	Franka Panda
Total episodes	454
Total frames	66,984
Total tasks	10
FPS	20
Total size	~300 MB
Codebase version	v3.0

Features

Feature	Dtype	Shape
observation.images.wrist_image	video	[256, 256, 3]
observation.images.image	video	[256, 256, 3]
observation.state	float32	[8]
action	float32	[7]
timestamp	float32	[1]
frame_index	int64	[1]
episode_index	int64	[1]
index	int64	[1]
task_index	int64	[1]

Tasks

task_index	Description
0	pick up the orange juice and place it in the basket
1	pick up the ketchup and place it in the basket
2	pick up the cream cheese and place it in the basket
3	pick up the bbq sauce and place it in the basket
4	pick up the alphabet soup and place it in the basket
5	pick up the milk and place it in the basket
6	pick up the salad dressing and place it in the basket
7	pick up the butter and place it in the basket
8	pick up the tomato sauce and place it in the basket
9	pick up the chocolate pudding and place it in the basket

Usage

from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset(
    "libero_object_no_noops_1.0.0_lerobot",
    root="/path/to/datasets/libero_object_no_noops_1.0.0_lerobot",
)

# Access a sample
sample = ds[0]
print(sample["observation.images.image"])  # [3, 256, 256]
print(sample["action"])                     # [7]
print(sample["task"])                       # natural language task description