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  1. PROCESSING.md +245 -0
  2. README.md +47 -0
  3. dataset_card.json +72 -0
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PROCESSING.md ADDED
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+ # How `block-stack` was produced
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+
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+ Raw teleoperation recordings of a **Swoosh right arm** (UFACTORY xArm 7) doing block
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+ manipulation, turned into LeRobot splits for world-model training.
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+
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+ Everything below is reproducible from this repo:
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+
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+ ```bash
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+ python -m quickdraw.data.processors +processor=block_stack \
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+ +source.dir=<raw campaign tree> +source.name=block_stack
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+ ```
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+
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+ Reader: [`src/quickdraw/data/block_stack.py`](https://github.com/isaac-ward/quickdraw/blob/main/src/quickdraw/data/block_stack.py) ·
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+ Processor: `block_stack()` in `src/quickdraw/data/processors.py`
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+
16
+ ---
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+
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+ ## 1. The robot and the teleop scheme
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+
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+ One xArm 7, right arm, **mounted 45° clockwise from vertical**. The mount is a fixed
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+ rotation between the arm's base frame and the world frame:
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+
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+ ```
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+ R_WORLD_FROM_BASE = [[1, 0, 0 ],
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+ [0, √½, -√½ ],
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+ [0, √½, √½ ]]
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+ ```
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+
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+ It is recorded in every run's provenance and verified against the physical arm (jog +X/+Y/+Z
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+ 20 mm, confirm the world axis that moves). The processor asserts the recorded matrix matches
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+ the one it assumes, so a re-mount cannot silently corrupt a rebuild.
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+
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+ An operator drove the arm with an **Xbox controller**:
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+
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+ | control | effect |
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+ |---|---|
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+ | left stick | planar motion parallel to the ground |
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+ | right stick Y | height of that plane |
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+ | right stick X | rotate the end-effector about the **world** vertical |
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+ | right trigger | gripper, proportional |
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+
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+ End-effector positioning: the pose goes to `set_servo_cartesian` and the **xArm controller
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+ solves the IK**. No IK is performed in this pipeline.
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+
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+ ## 2. What was recorded, and at what rate
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+
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+ The collection side writes five independent JSONL streams plus four cameras. **All of them
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+ share one monotonic clock origin** (`t_loop0`), recorded in `run.json`.
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+
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+ | stream | rate | contents |
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+ |---|---|---|
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+ | `controller` | 100 Hz | Xbox axes, post-deadzone (0.12) and post-expo (2.0) |
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+ | `commanded` | 100 Hz | the integrated target pose the stick asked for |
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+ | `xarm_command` | 100 Hz | the literal SDK arguments sent |
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+ | `arm_state` | 50 Hz | what the arm reported back |
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+ | `tick` | 100 Hz | control-loop timing |
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+ | 4 × camera | 30 Hz | MJPG → MP4, plus one timestamp per frame |
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+
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+ Cameras are two scene views and two on the right gripper:
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+ `scene_left`, `scene_right`, `gripper_right_bottom`, `gripper_right_top`, 640×480 @ 30 fps.
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+
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+ **Camera timestamps come from the V4L2 kernel buffer at capture**, not from when userspace
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+ received the frame, so the ~21 ms read lag is removed at the source.
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+
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+ ## 3. Synchronisation
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+
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+ There is **no cross-stream alignment step, deliberately** — no cross-correlation, no learned
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+ offset, no shifting. One clock plus kernel-level camera stamps means the streams are already
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+ on the same timeline. All the processor does is **resample**.
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+
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+ 1. **Grid.** 30 Hz (= the camera rate), spanning `[t_lo, t_hi]` where `t_lo` is the *latest*
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+ start and `t_hi` the *earliest* end across all six streams. Cameras open staggered 0.25 s
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+ apart, so this trims 1–2 s off the front of each run — which is why usable duration is
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+ shorter than the wall-clock A-to-B duration in `run.json`.
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+ 2. **Nearest-in-time, never interpolated.** Every source runs 1.7–3.3× the grid rate, so
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+ interpolation would invent precision. It would also be actively wrong for `joints_real_deg`,
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+ for rotation components, and for the gripper's 20 Hz poll.
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+ 3. **Per-camera index map.** Each camera's timestamps are matched to the grid to decide which
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+ MP4 frame becomes row *i*, then decoded in one forward pass.
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+
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+ Resampling displacement is reported per stream as max, p99, and the fraction past that stream's
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+ *own* half-period (the floor for any nearest-neighbour resample). On a representative 125 s run:
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+
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+ | stream | max | p99 | own bound | past bound |
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+ |---|---|---|---|---|
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+ | controller | 22.3 ms | 7.0 ms | 5.0 ms | 3.3% |
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+ | arm_state | 33.7 ms | 9.4 ms | 10.0 ms | 0.4% |
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+ | scene_left | 17.7 ms | 17.6 ms | 16.0 ms | 25.3% |
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+ | scene_right | 7.8 ms | 7.8 ms | 16.0 ms | 0.0% |
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+ | gripper_right_bottom | 18.0 ms | 18.0 ms | 16.0 ms | 20.6% |
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+ | gripper_right_top | 4.3 ms | 4.2 ms | 16.0 ms | 0.0% |
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+
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+ The camera rows are **phase, not fault**. A camera whose true rate differs from 30 Hz by a
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+ fraction of a percent drifts through the grid's phase, so its displacement sweeps the full
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+ ±half-period; `max ≈ half the camera period` is the arithmetic floor. The controller and arm
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+ maxima are isolated stream hiccups (`dt > 3×` median), two and one respectively in that run.
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+
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+ ## 4. `action` — 5 dims, the controller
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+
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+ ```
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+ [move_x, move_y, height, yaw, gripper]
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+ ```
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+
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+ `move_x, move_y, height, yaw` ∈ [−1, 1]; `gripper` ∈ [0, 1] where **1 = squeeze**.
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+
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+ **This is the raw human input, not a robot command.** The commanded pose and the literal SDK
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+ arguments were both recorded and are both deliberately excluded — the target pose at time *t*
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+ is `target[t−1] + stick × rate × dt`, so feeding it as an observation hands a world model the
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+ answer to "where does the arm go next". Both remain in the raw recordings.
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+
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+ ## 5. `observation_vector` — 17 dims, the arm
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+
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+ | idx | dims | field | source | transform |
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+ |---|---|---|---|---|
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+ | 0:3 | 3 | `ee_{x,y,z}_mm` | `pose_world_xyz_mm` | none; mm, world frame |
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+ | 3:9 | 6 | `ee_rot6_*` | `pose_base_mm_deg[3:6]` | RPY → rotation matrix → world frame → first two columns |
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+ | 9 | 1 | `gripper` | `gripper_pos` | `(x − closed)/(open − closed)`, **1 = open** |
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+ | 10:17 | 7 | `joint{1..7}_rad` | `joints_real_deg` | degrees → radians |
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+
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+ Three choices that are not obvious:
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+
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+ - **`joints_real_deg`, not `joints_deg`.** The latter is the controller's *planned* angle;
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+ on an earlier corpus the two were measured diverging by up to 5.02°.
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+ - **6D rotation, not Euler** ([Zhou et al.](https://arxiv.org/abs/1812.07035) — the first two
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+ columns of the rotation matrix; the third is recoverable by cross product, so nothing is
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+ lost). Euler angles wrapped 702 times in a single stream on an earlier corpus, and every
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+ wrap is a discontinuity a model must spend capacity memorising. Verified orthonormal on this
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+ data: column norms exactly 1.0, column dot product 7e-08.
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+ - **Gripper polarity is inverted between the two streams.** Action `gripper` 1 = squeeze;
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+ `gripper_pos` 850 = open. The state is normalised so 1 = open (monotonic in aperture) and
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+ the action is left as recorded. They are different quantities and were not collapsed.
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+
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+ ### A note on the rotation dimensions
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+
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+ The orientation is effectively **1 degree of freedom** in this corpus. Across all 56 runs the
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+ tool axis stays **37.5°–50.5° from straight down with std 0.2°** — only its azimuth changes,
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+ driven by the operator's right stick (111.6° of range). Singular values of the centred 6D
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+ cloud are `[1.00, 0.31, 0.009, 0.004, 0.002, 0.001]`.
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+
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+ The full 6D is kept anyway, because it is exact and assumption-free: a pure-yaw
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+ reconstruction `R = Rz(θ) · R₀` fits to mean 0.096° but **max 11.3°**, so there are real tilt
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+ excursions that a 2-dim yaw encoding would discard.
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+
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+ **Consumers should be aware** that `ee_rot6_2` and `ee_rot6_5` have std ≈ 0.003 — roughly 100×
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+ smaller than the other dims. The `normalization_stats.json` shipped here is a plain z-score,
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+ which therefore amplifies those two by ~324× and ~302×, turning sensor noise into a
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+ full-amplitude channel. **If you z-score this state vector, floor those two standard
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+ deviations or drop the two dims.** The parquet holds exact raw values, so any normalisation
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+ scheme can be recomputed.
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+
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+ ## 6. What was dropped
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+
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+ Nothing is destroyed — every raw stream is retained upstream and any of this can be restored
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+ by a re-run.
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+
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+ | stream | dropped | why |
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+ |---|---|---|
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+ | `commanded` | all 6 fields | a consequence of action + integrator state; leaks the answer |
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+ | `xarm_command` | all 7 fields | same, plus SDK return codes |
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+ | `tick` | all 4 fields | loop-health telemetry, not physics |
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+ | `controller` | `raw`, `raw_age_s`, `input_age_s`, `connected` | pre-shaping axis values and liveness |
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+ | `arm_state` | `joints_deg` | the *planned* angles (see above) |
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+ | `arm_state` | `pose_base_mm_deg[0:3]` | **exactly recoverable**: `R_WORLD_FROM_BASE @ base_xyz == pose_world_xyz_mm` to the digit |
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+ | `arm_state` | `report_alive`, `gripper_pos_t`, `gripper_pos_age_s`, `state`, `mode`, `error_code`, `warn_code` | liveness and fault telemetry, constant in all published runs |
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+
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+ ## 7. Campaigns and splits
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+
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+ Nine recording campaigns plus bring-up folders. Campaigns 1–2 (bring-up), `shakedown`, `audit`
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+ and four stray test artefacts were excluded. **Every published run passes all 32 of the
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+ collection side's own validation checks.**
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+
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+ The per-episode `task` field carries the campaign name into `<split>/meta/tasks.parquet`, so
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+ any split can be sliced by recording condition.
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+
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+ | split | source campaigns | episodes | frames |
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+ |---|---|---|---|
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+ | `train` | 3-play, 4-rgb, 5-play-long, 6-combos, 7-precision | 43 | 172,835 |
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+ | `val` | (the longest episodes of the same pool) | 2 | 34,799 |
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+ | `eval_purple_play` | 8-purple-play | 5 | 9,048 |
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+ | `eval_purple_stack` | 9-purple-stack | 6 | 5,323 |
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+
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+ `eval_*` are held-out conditions, written verbatim with no random splitting.
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+
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+ ### The train/val rule: longest trajectories to val
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+
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+ Val is **not** a random sample. It is the longest episodes, taken as the prefix whose frame
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+ share lands closest to 10%, with a floor of two episodes.
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+
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+ **Why.** Open-loop rollout evaluation can only run as far as the *shortest* validation
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+ episode — past that there is no ground truth to score against. On an earlier corpus a random
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+ seed-0 draw pulled a short episode into val and capped every long-horizon number at a fifth of
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+ the horizon the data actually supported.
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+
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+ Here train's longest episode is 15,996 steps and val's shortest is **16,606 steps ≈ 9 minutes**,
195
+ so the ordering is strict and rollouts are evaluable to nine minutes. The realised split is
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+ **83.2 / 16.8 by frames** rather than exactly 90/10 — the two longest episodes are much longer
197
+ than the rest, and the two-episode floor binds.
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+
199
+ **Read val loss accordingly.** Both val episodes come from `campaign5-play-long`. Val measures
200
+ long-horizon fidelity on long play trajectories; it is a rollout yardstick, not a
201
+ representative i.i.d. estimate of the training distribution.
202
+
203
+ ## 8. Verification
204
+
205
+ `tests/test_block_stack_roundtrip.py` checks a built dataset against a fresh read of the
206
+ source recordings.
207
+
208
+ **Vectors are bit-exact.** `max |diff| == 0.0` for both state and action across all 18,193 rows
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+ of val episode 0. No silent recast, reorder or truncation.
210
+
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+ **Frames align with state rows.** Scored as a shift sweep, not a single number — mean |Δpixel|
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+ of built frames against source frames at integer offsets:
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+
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+ | camera | −3 | −2 | −1 | **0** | +1 | +2 | +3 |
215
+ |---|---|---|---|---|---|---|---|
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+ | `scene_left` | 6.81 | 5.69 | 4.31 | **3.02** | 4.24 | 5.55 | 6.61 |
217
+ | `gripper_right_top` | 5.46 | 4.59 | 3.76 | **3.28** | 3.84 | 4.66 | 5.51 |
218
+
219
+ The residual 3/255 at offset 0 is video re-encode loss. **The shape is the result**: a strict
220
+ minimum at 0, rising cleanly either side. A small number alone would prove nothing, because a
221
+ dataset misaligned by one row also scores "small" on a slowly-moving scene. The test asserts
222
+ both the argmin *and* that the curve is steep enough to discriminate, so it cannot pass
223
+ vacuously.
224
+
225
+ ## 9. Known issues
226
+
227
+ - **`normalization_stats.json` z-scores two near-constant rotation dims** — see §5. Floor them
228
+ or drop them; the parquet values are exact.
229
+ - **`media/`** holds per-episode preview clips at build resolution. They duplicate the split
230
+ videos and exist so a human can see what a split contains; they are not needed for training.
231
+ - **LeRobot's own reader needs `torchcodec`**, which needs ffmpeg's shared libraries. Any
232
+ MP4-capable decoder reads these files; `quickdraw`'s loader uses `imageio`.
233
+
234
+ ## 10. Summary of the numbers
235
+
236
+ | | |
237
+ |---|---|
238
+ | robot data | **2.056 hours** (7,400 s) |
239
+ | episodes | 56 |
240
+ | frames | 222,005 |
241
+ | rate | 30 Hz |
242
+ | cameras | 4 × 144×192 (from 640×480) |
243
+ | observation | 17 dims |
244
+ | action | 5 dims |
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+ | raw corpus | 21 GB |
README.md ADDED
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1
+ ---
2
+ license: mit
3
+ pretty_name: block-stack
4
+ tags:
5
+ - world-models
6
+ - robotics
7
+ configs:
8
+ - config_name: train
9
+ data_files: train/data/**/*.parquet
10
+ - config_name: val
11
+ data_files: val/data/**/*.parquet
12
+ - config_name: eval_purple_play
13
+ data_files: eval_purple_play/data/**/*.parquet
14
+ - config_name: eval_purple_stack
15
+ data_files: eval_purple_stack/data/**/*.parquet
16
+ ---
17
+
18
+ # block-stack
19
+
20
+ Recorded trajectories (no simulator) packaged as LeRobot splits for world-model training.
21
+ Generated with [quickdraw](https://github.com/isaac-ward/quickdraw) (`data.processors`).
22
+
23
+ ## Observation / action
24
+ - **observation_vector** (17): the recorded state
25
+ - **observation.images.scene_left** (144×192×3, video): recorded camera stream, stored as MP4, aligned
26
+ 1:1 with the vector frames
27
+ - **observation.images.scene_right** (144×192×3, video): recorded camera stream, stored as MP4, aligned
28
+ 1:1 with the vector frames
29
+ - **observation.images.gripper_right_bottom** (144×192×3, video): recorded camera stream, stored as MP4, aligned
30
+ 1:1 with the vector frames
31
+ - **observation.images.gripper_right_top** (144×192×3, video): recorded camera stream, stored as MP4, aligned
32
+ 1:1 with the vector frames
33
+ - **action** (5): the recorded actions, at 30 Hz
34
+
35
+ ## Splits
36
+ | split | episodes | steps/ep (mean) | frames |
37
+ |---|---|---|---|
38
+ | `train` | 43 | 4019 | 172835 |
39
+ | `val` | 2 | 17400 | 34799 |
40
+ | `eval_purple_play` | 5 | 1810 | 9048 |
41
+ | `eval_purple_stack` | 6 | 887 | 5323 |
42
+
43
+ Normalization statistics are computed on **train only** and applied to every split.
44
+
45
+ ## How this dataset was produced
46
+
47
+ See **[PROCESSING.md](PROCESSING.md)** for the full account: source streams and rates, exactly which fields were kept and dropped and why, the resampling and synchronisation procedure, the train/val split rule, and the verification that images align with state rows.
dataset_card.json ADDED
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1
+ {
2
+ "base_env": {
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+ "obs_dim": 17,
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+ "action_dim": 5,
5
+ "dt": 0.03333333333333333,
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+ "position_idx": null,
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+ "velocity_idx": null,
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+ "dynamics_prior": false,
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+ "quat_idx": null,
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+ "mass": 12000.0,
11
+ "raw_dt": 0.05,
12
+ "dt_eff": 0.25,
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+ "R": 1.0,
14
+ "r": 1.0,
15
+ "init_speed": 1.0
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+ },
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+ "split_env": {
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+ "train": {
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+ "obs_dim": 17,
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+ "action_dim": 5,
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+ "dt": 0.03333333333333333
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+ },
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+ "val": {
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+ "obs_dim": 17,
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+ "action_dim": 5,
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+ "dt": 0.03333333333333333
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+ },
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+ "eval_purple_play": {
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+ "obs_dim": 17,
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+ "action_dim": 5,
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+ "dt": 0.03333333333333333
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+ },
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