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 from target_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_states messages
• ~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 as joint_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.