XYZPIT/260826_pick_up_the_bread_bag
Pick Up the Bread Bag Dataset Description This dataset contains teleoperated bread-bag pickup demonstrations collected using the Galaxea R1 Lite robot. In each episode, the robot picks up a bread bag from a conveyor-belt setup. The dataset is stored in the LeRobot dataset format and contains synchronized multi-view RGB videos, robot observations, and action commands. The dataset contains a single task instruction: pick up the bread bag Dataset Summary… See the full description on the dataset page: https://huggingface.co/datasets/XYZPIT/260826_pick_up_the_bread_bag.
Pick Up the Bread Bag
Dataset Description
This dataset contains teleoperated bread-bag pickup demonstrations collected using the Galaxea R1 Lite robot.
In each episode, the robot picks up a bread bag from a conveyor-belt setup. The dataset is stored in the LeRobot dataset format and contains synchronized multi-view RGB videos, robot observations, and action commands.
The dataset contains a single task instruction:
pick up the bread bag
Dataset Summary
Task Distribution
Camera Observations
Each episode contains four synchronized RGB camera streams.
The videos are encoded using AV1 at 31 FPS.
Robot Observations
The dataset includes robot proprioceptive observations such as:
- Left and right arm joint positions
- Left and right arm joint velocities
- Left and right gripper states
- Left and right end-effector poses
- Chassis state
- Torso state
- Chassis and torso velocities
- IMU measurements
Actions
The action features include:
- Left and right arm joint targets
- Left and right gripper targets
- Chassis velocity commands
- Torso velocity commands
Intended Use
This dataset is intended for research on robot imitation learning and vision-based manipulation, particularly for learning bread-bag pickup behavior in a conveyor-belt environment.
Limitations
- The demonstrations were collected using a single Galaxea R1 Lite robot.
- The data were collected in a single workspace and conveyor-belt configuration.
- The dataset contains only one task instruction.
- The dataset has limited variation in objects and environmental conditions.
- Performance learned from this dataset may not directly generalize to different objects, conveyor configurations, lighting conditions, backgrounds, or robot embodiments.
