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Domain: simulation · Environments: robosuite, single 6-DoF SO-101 arm (env-sim-robosuite-so101), Simulated 17-DoF XLeRobot platform (env-sim-xlerobot) · Format: LeRobot v3.0

Layout is <environment>/<task>/ — every leaf is a complete LeRobot dataset root.

Folder Task Kind Description Environment Episodes Frames FPS Robot
sim-robosuite-so101/t1_place_cup t1 manipulation Place the cup on the machine platform robosuite, single 6-DoF SO-101 arm (sim-robosuite-so101) 50 10,231 20 robosuite_sim
sim-robosuite-so101/t3_cup_to_tray t3 manipulation Move the filled cup from the machine to the tray robosuite, single 6-DoF SO-101 arm (sim-robosuite-so101) 50 19,485 20 robosuite_sim
sim-robosuite-so101/t5_tray_to_table t5 manipulation Move the filled cup from the tray to the user's table robosuite, single 6-DoF SO-101 arm (sim-robosuite-so101) 50 17,567 20 robosuite_sim
sim-xlerobot/t1_place_cup t1 manipulation Place the cup on the machine platform Simulated 17-DoF XLeRobot platform (sim-xlerobot) 51 17,988 20 xlerobot_sim
sim-xlerobot/t2_push_button t2 manipulation Push the machine's brew button Simulated 17-DoF XLeRobot platform (sim-xlerobot) 50 7,050 20 xlerobot_sim
sim-xlerobot/t3_cup_to_tray t3 manipulation Move the filled cup from the machine to the tray Simulated 17-DoF XLeRobot platform (sim-xlerobot) 50 19,209 20 xlerobot_sim
sim-xlerobot/t5_tray_to_table t5 manipulation Move the filled cup from the tray to the user's table Simulated 17-DoF XLeRobot platform (sim-xlerobot) 50 17,250 20 xlerobot_sim

Not yet uploaded: t4_navigate (t4).

Per-frame features

Feature dtype shape Meaning
action float32 [6] Commanded targets — 17-D on XLeRobot (left/right arm ×6, head ×2, base x/y/theta vel); 6-D on SO-101 (shoulder_pan, shoulder_lift, elbow_flex, wrist_flex, wrist_roll, gripper).
observation.state float32 [117] RAW telemetry — 84-D on XLeRobot (17 motors × {pos, current_raw, load_raw, vel_hw} + base x/y/theta vel + 13 GY-91 IMU channels; imu.mag_* is NaN, an MPU6500 has no magnetometer). 117-D on SO-101/sim, which additionally bakes in the EKF breakdown (estimated/tau_ext, model/tau_*, tcp/wrench/, hpi/gripper/).
observation.sim_model_force float32 [36] Ground truth, sim only (36-D): per-joint model decomposition (tau_motor, tau_gravity, tau_coriolis, tau_inertial, tau_friction, tau_model).
observation.sim_contact_force float32 [24] Ground truth, sim only (24-D): per-joint true external torque (tau_ext, tau_ext_residual, tau_ext_jac, tau_ext_contact) — the label an estimator is graded against.
observation.sim_tcp_wrench float32 [12] Ground truth, sim only (12-D): true TCP wrench, contact and F/T-sensor variants.
observation.sim_pose float32 [7] Ground truth, sim only (7-D): cup position + quaternion.
observation.sim_task float32 [2] Ground truth, sim only (2-D): task success / failure flags.
observation.hpi float32 [9] Force channel (9-D): [grip tau_ext, grip q, grip dq, TCP wrench fx fy fz tx ty tz] — input to the force-aware (A1) models. NOT present on XLeRobot platform data: estimation there is a deliberate offline step so the estimator stays an experimental axis.
observation.images.top video [480, 640, 3] Head / overview camera (RGB).
observation.images.top_depth image [480, 640, 1] Overview depth (int16, 1-channel). Not used by ACT; dropped when building training variants.
observation.images.head video [480, 640, 3] Head / overview camera (RGB).
observation.images.head_depth image [480, 640, 1] Overview depth (int16, 1-channel). Not used by ACT; dropped when building training variants.

Load one leaf (allow_patterns matters — without it you pull the whole repo):

from huggingface_hub import snapshot_download
from lerobot.datasets.lerobot_dataset import LeRobotDataset

repo, leaf = "IntelligentDecisionLab/xlerobot-coffee-sim", "sim-robosuite-so101/t1_place_cup"
root = snapshot_download(repo, repo_type="dataset", allow_patterns=f"{leaf}/*")
ds = LeRobotDataset(repo, root=f"{root}/{leaf}")
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