license: apache-2.0
task_categories:
- robotics
tags:
- teleoperation
- dual-arm
- mcap
- TeleXperience
- DualArxR5a
- t-shirt-folding
FoldingTShirt_DualArxR5a_Samples
100 real-robot teleoperation episodes for “Fold the T-shirt on the table.” on a DualArxR5a dual-arm robot. Format: raw MCAP (ROS 2 / rosbag2).
Source
Collected with TeleXperience, IO-AI’s product for real-robot teleoperation and data collection. An operator drives the robot; TeleXperience writes time-aligned RGB, joint commands, joint states, gripper targets, and end-effector poses to MCAP.
Product page: https://io-ai.tech/en/telexperience/
Task: Fold the T-shirt on the table.
Robot: DualArxR5a (bimanual, parallel-jaw grippers)
Language prompt: Fold the T-shirt on the table.
Repository layout
FoldingTShirt_DualArxR5a_Samples/
README.md
telexperience_preview.mp4
manifest.json
data/
<episode>.mcap
<episode>.metadata.yaml
Episode file names keep the original TeleXperience recording id, for example:
livingroom_T-shirt_folding_DualArxR5a_251110_145215_0.mcap
Each .metadata.yaml is the rosbag2 inventory written at capture time (duration, topic list, message counts). Per-episode stats are also in manifest.json.
MCAP data structure
Every episode is a self-contained MCAP with the same topic set.
| Topic | Type | Role | Typical rate |
|---|---|---|---|
/camera_high/color/image_raw/compressed |
sensor_msgs/CompressedImage |
High / head RGB | ~30 Hz |
/camera_low/color/image_raw/compressed |
sensor_msgs/CompressedImage |
Low / front RGB | ~30 Hz |
/camera_left_wrist/color/image_raw/compressed |
sensor_msgs/CompressedImage |
Left-arm wrist RGB | ~30 Hz |
/camera_right_wrist/color/image_raw/compressed |
sensor_msgs/CompressedImage |
Right-arm wrist RGB | ~30 Hz |
io_teleop/joint_cmd |
sensor_msgs/JointState |
Teleop joint command (action) | ~100 Hz |
io_teleop/joint_states |
sensor_msgs/JointState |
Robot joint state (proprioception) | ~100 Hz |
io_teleop/target_gripper_status |
sensor_msgs/JointState |
Gripper command (left_gripper, right_gripper) |
~100 Hz |
io_teleop/target_ee_poses |
geometry_msgs/PoseArray |
Target end-effector poses | ~100 Hz |
io_teleop/robot_info |
std_msgs/String |
Robot / session metadata (DualArxR5a) |
1 message |
Joint command and state (1:1)
io_teleop/joint_cmd and io_teleop/joint_states use the same 14 names, same order, same units:
left_joint1, left_joint2, left_joint3, left_joint4, left_joint5, left_joint6, left_joint7,
right_joint1, right_joint2, right_joint3, right_joint4, right_joint5, right_joint6, right_joint7
*_joint1…*_joint6: arm joints in radians*_joint7: gripper in 0 / 1 (open / closed), taken fromtarget_gripper_status- Look up by name; do not assume a converter-specific left/right packing
target_gripper_status.names is left_gripper, right_gripper, also 0 / 1.
Camera positions
All four streams are 640×480 JPEG, synchronized at about 30 Hz.
| Topic | Mount | What you see |
|---|---|---|
/camera_high/color/image_raw/compressed |
High / head, looking down at the workspace | Full white table, the T-shirt, both grippers, room background. Most stable view. |
/camera_low/color/image_raw/compressed |
Low / front (chest), closer to the table | Same scene from a lower, nearer angle. Grippers occupy more of the bottom of the image. |
/camera_left_wrist/color/image_raw/compressed |
Left wrist (eye-in-hand) | Close-up of the left gripper and the fabric it is working on. |
/camera_right_wrist/color/image_raw/compressed |
Right wrist (eye-in-hand) | Close-up of the right gripper and the fabric it is working on. |
Suggested LeRobot names if you convert this MCAP: camera_high, camera_low, camera_left_wrist, camera_right_wrist.
A typical episode has:
- ~2,000 frames per camera
- ~6,500–8,000
joint_cmd/joint_statesmessages - ~40k messages in total
- ~60–80 seconds of wall time
Training fields
- observation:
camera_high+camera_low+camera_left_wrist+camera_right_wrist+joint_states - action:
joint_cmd(identical names / order / units asjoint_states) - language:
Fold the T-shirt on the table.
License
Apache License 2.0
Citation
If you use this sample, please mention TeleXperience and the dataset name FoldingTShirt_DualArxR5a_Samples.