Lidar-Tactile — Raw ROS 2 MCAP recordings

Raw rosbag2 MCAP recordings from a Unitree G1 humanoid performing bimanual tactile loco-manipulation with ViTac/sensx force sensors. These are the unprocessed bags; the segmented training zarrs derived from them live in GeorgiaTech/ik-modulation-zarr.

Layout

mcap/<task>/<stream>/<run>/<traj>/*.mcap   (+ metadata.yaml per bag)

mcap/bucket/ — bucket manipulation task (2026-05)

Stream	Path	Count
Teleop	mcap/bucket/teleop/2026-05-09-17-08-12/{001..056}/	56 trajectories
Human	mcap/bucket/human/{003..020}/	15 trajectories

• Teleop: operator drives the robot; bags contain the commanded IK target (/g1_upper_pink_controller/{left,right}_hand), the measured EE (/tf_server_{left,right}/pose_measured), IK metrics, and tactile force/raw.
• Human: human demonstration; measured hand poses + tactile force/raw.

Key topics

Quantity	Topic
Measured EE (teleop)	/tf_server_{left,right}/pose_measured
IK target (teleop)	/g1_upper_pink_controller/{left,right}_hand
Tactile force	/sensor1/sensor1/force (left), /sensor2/sensor2/force (right), /chest_sensor/...
Tactile raw	/sensor{1,2}/.../raw, /chest_sensor/.../raw

Sensor mapping: sensor1 = left hand, sensor2 = right hand.

Reading a bag

from mcap_ros2.decoder import DecoderFactory
from mcap.reader import make_reader
with open("mcap/bucket/teleop/2026-05-09-17-08-12/001/001_0.mcap", "rb") as f:
    reader = make_reader(f, decoder_factories=[DecoderFactory()])
    for schema, channel, message, ros_msg in reader.iter_decoded_messages():
        ...  # channel.topic, ros_msg fields

See the conversion pipeline writeup (ik_modulation/configs/bucket_pipeline/BUCKET_PIPELINE.md in the diffusion_humanoid_train repo) for how these bags become segmented training zarrs.