--- license: apache-2.0 task_categories: - robotics tags: - lerobot - imitation-learning - ur7e - manipulation - pick-and-place - hdf5 - teleoperation size_categories: - 1K **Faster than the banana session.** The robot-side streams here run at **~97–98 Hz**, notably > faster than the ~56–60 Hz of `banana_in_pot_raw`. Anyone comparing the two datasets will see > roughly **3.2 robot samples per camera frame** here versus ~2 there. Never assume a fixed dt — > always resample against `t_rel_s`. > **Sampling is bursty, not uniform.** For the robot streams the inter-sample interval is > *bimodal*: about 5 % of intervals are ~2.9 ms and the median is ~13.3 ms. That is why the > **mean** rate (98 Hz) is higher than the rate implied by the **median** interval (~76 Hz). Both > figures are correct; they describe different things. `dataset_stats.json` reports both. **Cameras:** `cam1.mp4` / `cam2.mp4`, 1280×720 RGB, 30 fps, MPEG-4. Use `cam*_frames` `frame_idx` / `t_rel_s` to align frames to the vector streams. > See **[`DATA_DICTIONARY.md`](DATA_DICTIONARY.md)** in this repo for the exhaustive per-field > listing (every dataset key, dtype, unit, and measured value range) and the per-take table. Notes: - `command` (cmd1..6) are the joint targets sent to the UR7e; `ur_joint_states.q*` are the measured follower joints. `gello_joint_states.q*` are the **leader** joints — provided for completeness but **not a valid inference input** (the deployed robot cannot see the leader). - The physical robot is a **UR7e** and the derived LeRobot dataset labels it correctly as `robot_type: "ur7e_gello"`. Note this differs from the older [`banana_in_pot_lerobot_v3`](https://huggingface.co/datasets/Bigenlight/banana_in_pot_lerobot_v3), which carries a legacy `"ur5e_gello"` label for the same physical arm. ## Measured value ranges Pooled over all 24 takes — the actual envelope this data covers. | signal | min | max | mean | |---|--:|--:|--:| | `ur_q1` (rad) | 2.6016 | 3.2402 | 2.8606 | | `ur_q2` (rad) | −1.7067 | −1.1299 | −1.4411 | | `ur_q3` (rad) | 1.4553 | 2.3131 | 1.9832 | | `ur_q4` (rad) | −2.6559 | −1.5613 | −2.2401 | | `ur_q5` (rad) | −1.7499 | −1.3693 | −1.5022 | | `ur_q6` (rad) | −3.7516 | −3.0714 | −3.4827 | | joint velocity `qd*` (rad/s) | −0.648 | 0.663 | ≈0 | | joint effort `eff*` | −7.084 | 5.802 | — | | TCP `x` (m) | 0.4199 | 0.5975 | 0.5090 | | TCP `y` (m) | −0.1456 | 0.1888 | −0.0019 | | TCP `z` (m) | 0.1785 | 0.4873 | 0.2971 | | force `fx,fy,fz` (N) | −143.19 | 63.42 | — | | torque `tx,ty,tz` (N·m) | −6.882 | 3.509 | — | | `grip_pos` (normalized) | 0.0118 | 0.6314 | 0.2183 | | `grip_cmd` (normalized) | 0.0000 | 0.9998 | 0.3788 | | `gello_grip` (normalized) | 0.0000 | 1.0000 | 0.3787 | The commanded joints `cmd1..cmd6` track the measured joints closely (e.g. `cmd1` spans 2.6016–3.2395 against `ur_q1`'s 2.6016–3.2402). The whole session lives in a **compact workspace**: ~18 cm of TCP travel in x, ~33 cm in y, ~31 cm in z. All takes stay inside the joint safety envelope. Per-channel min/max/mean/std for **every** channel is in `dataset_stats.json` and `DATA_DICTIONARY.md`. ## How the derived dataset is built from this | dataset | contents | |---|---| | **raw (this repo)** | full multi-rate HDF5 + 2 MP4s — every signal above, native rates, **24** takes | | [LeRobot joint](https://huggingface.co/datasets/Bigenlight/cube_in_cup_lerobot_v3) | `observation.state`(7)=`ur_q1..6`+`grip_pos`; `action`(7)=`cmd1..6`+`grip_cmd`; 2 AV1 videos, **23** episodes | The LeRobot dataset is built with the same `convert_to_lerobot.py` recipe as the banana family: it **resamples every stream onto the 30 fps cam1 timestamp grid** (nearest timestamp), drops the `gello_*` leader streams, and re-encodes the videos to AV1. It has **23 episodes, not 24** — `take_23` is excluded there (see below). This raw release keeps everything at native rate so you can resample or add features yourself. ## Usage Read a take with `h5py`: ```python import h5py, cv2, numpy as np take = "take_03_20260720_205457" with h5py.File(f"{take}/vectors.h5", "r") as f: ur_q = np.stack([f["ur_joint_states"][f"q{k+1}"][:] for k in range(6)], axis=1) # (N97, 6) rad cmd = np.stack([f["command"][f"cmd{k+1}"][:] for k in range(6)], axis=1) # (N98, 6) rad tcp = np.stack([f["tcp_pose"][k][:] for k in "x y z qw qx qy qz".split()], 1) # (N97, 7) wrench = np.stack([f["wrench"][k][:] for k in "fx fy fz tx ty tz".split()], 1) # (N97, 6) grip = f["gripper"]["grip_pos"][:] # (N36,) cam1_t = f["cam1_frames"]["t_rel_s"][:] # (Ncam,) 30 Hz # each stream has its own f[group]["t_rel_s"] — align by nearest timestamp cap = cv2.VideoCapture(f"{take}/cam1.mp4") # 1280x720 RGB @ 30 fps ``` To go straight to training, use the ready LeRobot dataset instead (`LeRobotDataset("Bigenlight/cube_in_cup_lerobot_v3")`). > ⚠️ **`columns` attribute quirk.** Every group carries an attribute named `columns` that is a > **single scalar JSON string**, not a list — verified `str` in all 24 files. Always > `json.loads(grp.attrs["columns"])`; `list(...)` on it iterates character-by-character and yields > garbage. ## Known quirks - **`take_23_20260720_210316` is a misfire — the one real defect in this release.** It is 1.64 s long (49 cam1 frames), the UR barely moves (per-joint range < 0.013 rad, i.e. essentially motionless), and the **gripper is never actuated** (`grip_pos` flat at 0.0118, `grip_cmd` flat at 0.0). It contains no demonstration of the task. It is **included here** because this is the raw, as-recorded release, and **excluded from** `cube_in_cup_lerobot_v3`. Drop it in any training pipeline built from this repo. - **Take-number gaps `{4, 6}` are by design.** Folders run `take_01` … `take_26` but takes 04 and 06 were aborted/discarded during recording, giving exactly **24** folders. Do not treat the gaps as missing data. - **Duration outliers, not defects:** `take_01` (16.82 s) and `take_02` (16.41 s) are about **2× the median take length** (8.24 s) — early, slower demonstrations. Their data is clean. - **`cam1` / `cam2` frame-count mismatch:** in **16 of 24** takes the two cameras differ, by **±1 frame** (±2 in `take_21`; totals 6,226 cam1 vs 6,231 cam2). The two cameras are on independent clocks — map between them by nearest timestamp; **never assume `cam1[k]` and `cam2[k]` are simultaneous.** Exact per-take deltas are in `DATA_DICTIONARY.md` and `dataset_stats.json`. - **`synchronized/` group is empty** (0 rows) in all 24 takes, despite declaring 56 channels. Fusion happens downstream at conversion time, not here. - **`gello_q6` is wrapped +2π relative to `ur_q6`.** Leader joints 1–5 track the follower to within ±0.011 rad, but joint 6 differs by **+6.2797 rad ≈ +2π** (`gello_q6` ∈ [2.53, 3.26] vs `ur_q6` ∈ [−3.75, −3.07]) — the same physical wrist angle in a different revolution. Subtract 2π before comparing leader to follower, or it looks like a massive tracking failure. - `grip_pos` is normalized and never reaches 1.0 — the observed open extreme is **0.6314** (in `take_09`), typical open is ~0.50, closed is 0.0118. This is the physical range the fingers actually swept, not a clipping artifact. ## Data quality Audited over all 24 takes: - **Zero NaN and zero Inf** in any channel of any group of any take. - **Video frame counts match `cam*_frames` row counts exactly** for all 24 takes, **both** cameras (48/48 videos) — verified with `ffprobe -count_frames`. - **All takes stay within the joint safety envelope** (see measured ranges above). - Timing is well behaved: the largest single gap in any robot stream is **44 ms**, and the largest in any camera stream is **65 ms** (~2 frame periods). Ratio of max to median interval is 1.2–3.2× per take. - Schema is **identical across all 24 files** — same 9 groups, same channel names, all `float64`. - Cleanliness: no stray files, no hidden files, no sub-directories inside the take folders, and no `/home/` absolute-path strings inside any of the 24 H5 files. No success/failure labels, no human quality ratings, and no policy-performance numbers are claimed for this dataset. ## Limitations & intended use - **Small.** 24 takes / ~3.5 minutes is a *pilot-scale* dataset. It is not enough on its own for a robust policy; treat it as a seed set or a schema reference. - Single task, single scene layout, single operator, single session, fixed object placement. - **No trained policy exists for this task yet.** - Streams are **asynchronous** (each has its own clock); you must resample/align them yourself — the `synchronized/` group is empty. - Sampling is bursty (see the rate note above), so nearest-timestamp alignment is required rather than index arithmetic. - Intended for research in imitation learning, teleoperation analysis, and custom dataset construction. ## Related repositories (this family) | repo | contents | |---|---| | [**Bigenlight/cube_in_cup_raw**](https://huggingface.co/datasets/Bigenlight/cube_in_cup_raw) | **this** — raw HDF5 + MP4, 24 takes | | [Bigenlight/cube_in_cup_lerobot_v3](https://huggingface.co/datasets/Bigenlight/cube_in_cup_lerobot_v3) | LeRobot joint-space dataset, 23 episodes | | [Bigenlight/banana_in_pot_raw](https://huggingface.co/datasets/Bigenlight/banana_in_pot_raw) | sibling raw dataset, same rig, different scene | ## Citation ```bibtex @misc{theo2026cubeincupraw, title = {cube_in_cup_raw: raw UR7e + GELLO teleoperation logs (HDF5 + MP4) for "put the cube in the cup"}, author = {Theo and {Bigenlight}}, year = {2026}, howpublished = {\url{https://huggingface.co/datasets/Bigenlight/cube_in_cup_raw}}, note = {24 takes, native multi-rate HDF5 + dual RGB MP4} } ``` License: **Apache-2.0**.