| --- |
| license: apache-2.0 |
| pretty_name: cablepilot motion |
| task_categories: [robotics] |
| tags: [robotics, world-model, cable-driven-parallel-robot, stringman, simulation, task-free] |
| size_categories: [10K<n<100K] |
| configs: [] |
| --- |
| |
| # cablepilot motion |
|
|
| The data that the [cablepilot](https://huggingface.co/MacroSwan/cablepilot) world model (`weights/stringman_g1.pt`, |
| 28k parameters) learned from. It contains **12,000 task-free trajectories**, about 100 hours of simulated motion of a |
| Stringman cable robot with its stock gripper. Each trajectory is 30 s long, recorded at 10 Hz, in **its own random |
| room**. There is no target, no cost and no task label: only how the machine moves when it is commanded. Tasks come |
| later, as cards ([`MacroSwan/pilot-tasks`](https://huggingface.co/datasets/MacroSwan/pilot-tasks)). |
|
|
| ## Files |
|
|
| `data/shard_XXXX.npz` holds 120 shards of 100 trajectories each, and `data/manifest.json` records the recipe, the |
| seed and the field names. |
|
|
| | key | shape | what | |
| |---|---|---| |
| | `states` | (N, 301, 14) | gantry position p and velocity v, pole swing s and its rate, wrist angle and rate, finger closure and rate. These are **as estimated** (with the estimator's noise), the way the robot would see them | |
| | `actions` | (N, 300, 5) | the command sent: gantry velocity (3, what `move_direction_speed` takes), wrist rate, closure target | |
| | `tensions` | (N, 301, 4) | line tensions (N). An observation only, not a model input | |
| | `payload` | (N, 301) | kg held at the grasp point. The model has to infer it | |
| | `eta` | (N, P) | this trajectory's randomised physics (names in the manifest) | |
| | `mounts` | (N, 4, 3) | the room's four line anchors, in the firmware's line order. This is how the room reaches the model | |
| | `runs` | (N, 4) | the fixed run length of each indirect line | |
|
|
| ## How it was made |
|
|
| `scripts/cdpr_datagen.py` in the model repository (its docstring gives every range): |
|
|
| ```bash |
| python scripts/cdpr_datagen.py --scenario configs/scenarios/clear_playroom.yaml --n 12000 --seed 0 --workers 4 --out data |
| ``` |
|
|
| The robot is driven the way the real host drives it. Line speeds come from finite-difference IK on the estimated |
| position, through mounts that are miscalibrated on purpose. The spool loop runs at 50 Hz with the tension floor. Motors |
| are acceleration-limited, and commands arrive with latency. The motion is a random chain of the robot's real regimes: |
| go to a point, dwell, stop hard, pump the swing, descend to grasp height, and MPPI-like command noise. Payloads from |
| 20 to 500 g are picked up and dropped. Each trajectory randomises mass, line stiffness and damping, spool acceleration, |
| latency, calibration error and swing damping. Rooms are 3 to 7 m jittered rectangles with mounts 2.0 to 3.2 m high. |
|
|
| This copy was regenerated from that script, seed 0, at the release commit (the original files were not kept). On its |
| last 500 trajectories, which are held out, the released weights score 20.4 mm median grasp-point error at 2 s, against |
| 21.5 mm for the MuJoCo oracle. The original test gave 19.6 mm against 21.0 mm. Both moved by the same half millimetre, |
| which fits the same distribution drawn with a different random sequence. |
|
|
| ## Use it |
|
|
| Retrain or fine-tune with `scripts/train_cdpr_wm.py --data <this folder>`. A model that beats the released one on |
| test 1 and on a task is welcome as a PR on `MacroSwan/cablepilot` (see its AGENTS.md, Step 7). Real Stringman |
| recordings, such as `naavox/stringman_cleans`, are the natural test set. Nobody has measured the model on real data |
| yet. |
|
|
| Apache-2.0. Simulated, so it has no people and no homes in it. |
|
|
