Bigenlight/banana_in_pot_ee_lerobot_v3

banana_in_pot_ee — UR7e teleop with TCP pose + wrench (LeRobot v3.0, EE variant)

The end-effector variant of banana_in_pot: the same 51 demonstrations of "put the right banana in the pot," with two extra observation streams — the 6-axis TCP pose and the 6-axis force/torque wrench — added so you can train end-effector-space policies (e.g. HIL-SERL EE-delta) without touching the raw logs.

• 51 episodes · 21,524 frames · 30 fps · ~11.96 min
• Format: LeRobot v3.0 (lerobot 0.6.1)
• Same episodes & videos as the main dataset; extra features observation.tcp_pose (7) and observation.wrench (6).

Setup

Collected on a Universal Robots UR7e — a 6-DOF collaborative arm (joints in radians) — driven by a GELLO 3D-printed leader arm for kinesthetic teleoperation. Two Intel RealSense cameras record the scene as RGB video only:

• Camera 1 — Intel RealSense D435
• Camera 2 — Intel RealSense D435if (a D435 variant)

Both stream 1280×720 (720p) @ 30 fps, yuv420p. One is on a tripod (scene view), the other views the workspace. No depth or IR was recorded — only the RGB color stream was saved. cam1 ↔ cam2 order must be preserved at deploy. An ArUco/AprilTag fiducial is on the table.

Task

"put the right banana in the pot." The scene contains distractors — two bananas, an apple, carrots/peppers, and a slice of watermelon — and a silver pot. The operator grasps the RIGHT banana (target) and places it in the pot.

• Success criterion: the right banana ends up inside the pot.
• Distractors: left banana, apple, carrots/peppers, watermelon slice.
• All 51 demos are successes.

Schema

feature	dtype	shape	meaning
observation.state	float32	(7,)	UR7e measured joints ur_q1..ur_q6 (rad) + grip_pos
observation.tcp_pose	float32	(7,)	TCP pose in robot base frame: x, y, z (m) + quaternion qw, qx, qy, qz
observation.wrench	float32	(6,)	end-effector force/torque: fx, fy, fz (N) + tx, ty, tz (N·m)
action	float32	(7,)	commanded absolute joint targets cmd1..cmd6 (rad) + grip_cmd
observation.images.cam1	video (AV1)	(720, 1280, 3)	viewpoint 1 (RGB, HWC uint8)
observation.images.cam2	video (AV1)	(720, 1280, 3)	viewpoint 2 (RGB, HWC uint8)

Plus the standard LeRobot bookkeeping columns. The two extra streams are resampled onto the same 30 fps camera clock as everything else. tcp_pose is the recorded RTDE TCP (validated to be the flange pose, sub-mm / sub-0.2° at rest); it is the trustworthy EE signal — no forward-kinematics approximation is involved.

Note: LeRobot metadata records robot_type: "ur5e_gello" (legacy label). The physical robot is a UR7e.

Why the EE variant

For end-effector-space RL / HIL-SERL, the policy action is a base-frame TCP displacement [Δx, Δy, Δz, gripper]. observation.tcp_pose gives you the per-step TCP position/orientation directly, so EE-delta targets can be derived as (p_{t+1} − p_t) / step_size (plus the normalized gripper) with no FK needed. The task is effectively a 3-DoF position + gripper problem (orientation held fixed in the RL pipeline). observation.wrench additionally exposes contact forces for force-aware or safety-gated policies.

Hardware & Collection

Identical pipeline to the main dataset: UR7e (6-DOF, radians) + GELLO leader arm; Intel RealSense D435 (cam1) + D435if (cam2), RGB only, 1280×720 @ 30 fps (yuv420p, MPEG-4 raw → AV1 here); no depth/IR.

Source stream rates (native, before resampling)

stream	native rate	in this dataset
cameras (cam1, cam2)	30 Hz	master 30 fps clock
command (UR joint targets)	~56 Hz	→ action[0:6]
ur_joint_states	~56 Hz	→ observation.state[0:6]
tcp_pose	~56 Hz	→ observation.tcp_pose (7)
wrench (6-axis F/T)	~56 Hz	→ observation.wrench (6)
gripper	~37 Hz	→ grip_pos / grip_cmd
gello_joint_states	~30 Hz	excluded (leader-only)

Every included stream is resampled onto the 30 fps camera grid by nearest-timestamp lookup on the cam1 timeline. gello_* leader streams are excluded. Original takes #9/23/30/35 are absent by design; re-takes fill the count to 51. Scale: 51 episodes / 21,524 frames / 718 s (12 min).

Known quirks

• 1 take had NaN grip_cmd (333 frames) — repaired by forward/back-fill.
• ~7 takes have raw robot-stream dropouts (75–280 ms), giving up to ~40 ms cam↔robot match error at those frames; mostly harmless.
• grip_pos peaks at 0.898 = fully-open gripper (physical).
• The recorded tcp_pose vs. FK shows a small timing-jitter offset on fast motions (a logging artifact between async streams, not a kinematic error); at rest it agrees to sub-mm. Use the recorded tcp_pose as ground truth.

Usage

from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("Bigenlight/banana_in_pot_ee_lerobot_v3")
sample = ds[0]
sample["observation.state"]      # (7,)  joints + grip
sample["observation.tcp_pose"]   # (7,)  x,y,z, qw,qx,qy,qz
sample["observation.wrench"]     # (6,)  fx,fy,fz, tx,ty,tz
sample["action"]                 # (7,)  absolute joint targets + grip

Train a joint-space ACT exactly as the main dataset (--dataset.repo_id=Bigenlight/banana_in_pot_ee_lerobot_v3 --policy.type=act …); the extra observations are available for EE-space methods. A trained joint-space ACT policy is at Bigenlight/act_banana_in_pot.

Related repositories (this family)

repo	contents
Bigenlight/banana_in_pot_lerobot_v3	main LeRobot joint-space dataset
Bigenlight/banana_in_pot_ee_lerobot_v3	this — adds observation.tcp_pose (7) + observation.wrench (6)
Bigenlight/banana_in_pot_raw	raw HDF5 + MP4 (all original signals, incl. GELLO leader)
Bigenlight/act_banana_in_pot	trained ACT policy

Limitations & intended use

• Single task / scene layout / operator; all demos are successes (no failure data).
• tcp_pose on fast motions carries a small timing-jitter offset (see quirks); best used as recorded rather than differentiated aggressively without smoothing.
• Offline metrics are strong but real-robot closed-loop is not guaranteed — validate on hardware.
• Intended for research in imitation learning, end-effector-space RL (HIL-SERL), and force-aware manipulation.

Citation

@misc{theo2026bananainpotee,
  title        = {banana_in_pot_ee: UR7e teleoperation demonstrations with TCP pose
                  and wrench for "put the right banana in the pot"},
  author       = {Theo and {Bigenlight}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/Bigenlight/banana_in_pot_ee_lerobot_v3}},
  note         = {LeRobot v3.0 dataset, 51 episodes, EE variant}
}

License: Apache-2.0.