DART regen: 13-dim goal-conditioned state-based v3.0 + analysis figures (FPV auxiliary)

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>


Co-authored-by: claude opus 4.8 <claude opus 4.8@users.noreply.huggingface.co>
Co-authored-by: claude opus 4.8 <claude opus 4.8@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+task_categories:
+- robotics
+tags:
+- LeRobot
+- ledrone
+- uav
+- drone
+- sim
+- pybullet
+- circle
+- imitation-learning
+- goal-conditioned
+- state-based
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  data_files: data/*/*.parquet
+---
+
+# ledrone_pybullet_circle
+
+A **goal-conditioned, state-based** imitation-learning dataset of a quadrotor
+performing the **circle** task (track a horizontal circular trajectory), generated in **PyBullet** rigid-body physics
+with a privileged analytic expert. Each frame pairs the drone's 13-dim proprioceptive
+state and a relative goal with the expert's velocity + yaw-rate command — ready to
+train goal-conditioned control policies
+([Diffusion Policy](https://diffusion-policy.cs.columbia.edu/), ACT, …). A
+first-person-view (FPV) camera is shipped as an **optional auxiliary** stream (the
+task is solvable from state alone).
+
+Built with [LeRobot](https://github.com/huggingface/lerobot) (format `v3.0`) as part
+of the [ledrone](https://github.com/ahive-org/ledrone) project.
+
+<a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path=ahive/ledrone_pybullet_circle">
+<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/>
+<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/>
+</a>
+
+## At a glance
+
+| | |
+|---|---|
+| Task | `circle` — track a horizontal circular trajectory |
+| Type | Goal-conditioned, **state-based** (FPV image is an optional auxiliary) |
+| Episodes | 300 |
+| Frames | 120,000 (400/episode, 13.3s at 30 Hz) |
+| Robot type | `ledrone` (quadrotor) |
+| Physics | PyBullet rigid-body dynamics via [gym-pybullet-drones](https://github.com/utiasDSL/gym-pybullet-drones) |
+| Expert | Analytic PD + reference-velocity feed-forward + course-to-goal yaw, slew-limited startup (privileged, **state-only**) |
+| Camera | FPV 96×96 RGB (AV1) — auxiliary; scene has a goal marker, landmarks, textured ground |
+| Action | NED velocity + yaw rate |
+| Format | LeRobot `v3.0` |
+
+## How it was generated
+
+A privileged analytic **state-only** expert (PD + reference-velocity feed-forward)
+flies the `circle` reference in the `LeDrone` env on the **PyBullet** backend.
+
+- **DART-style coverage** ([Laskey et al. 2017](https://arxiv.org/abs/1703.09327)): the
+  drone is driven by a *noised* command (σ = 0.15 m/s on the velocity channels) but each
+  frame is **labelled with the clean expert command** — so the policy sees
+  off-distribution states *and* the optimal correction for them, mitigating
+  behavior-cloning covariate shift.
+- **Slew-rate-limited setpoint** ramps the command from rest, taming the low-level
+  controller's start-of-episode transient (early-episode tilt/rate spikes).
+- **Controlled heading** (course-to-goal yaw); **randomized** start + goal per episode;
+  the goal is stored **relative to current position** (origin-invariant).
+- **Convergence gate**: an episode is accepted only once the expert reaches / tracks the
+  goal (else the start/goal is resampled).
+
+Reproduce (needs the `[pybullet]` extra):
+
+```bash
+python tools/generate_expert_dataset.py --task circle --backend pybullet \
+    --repo-id ahive/ledrone_pybullet_circle --episodes 300 --push-to-hub --public
+```
+
+## Features
+
+| Key | Dtype | Shape | Description |
+|---|---|---|---|
+| `observation.state` | float32 | (13,) | `pos_n/e/d`, `vel_n/e/d`, attitude quat `q_w/x/y/z`, FRD body rates `rate_x/y/z` |
+| `observation.environment_state` | float32 | (3,) | Goal relative to current position: `err_n`, `err_e`, `err_d` |
+| `action` | float32 | (4,) | Command: `vx`, `vy`, `vz`, `yaw_rate` |
+| `observation.images.fpv` | video | (96, 96, 3) | Forward-facing FPV camera, AV1 (**auxiliary**) |
+
+State is the SOTA quadrotor policy observation (relative goal + velocity + orientation +
+body rates); **battery is intentionally excluded** (handled by domain randomization, not
+observed — Hwangbo 2017, Molchanov 2019, Kaufmann ICRA 2022 / Nature 2023). Attitude is
+a unit quaternion; map to a continuous 6D rep
+([Zhou et al., CVPR 2019](https://arxiv.org/abs/1812.07035)) if desired. `action` is a
+NED velocity + yaw rate clipped to the envelope (≤ 2 m/s horizontal, ≤ 1 m/s climb,
+≤ 45 °/s yaw).
+
+## Usage
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+ds = LeRobotDataset("ahive/ledrone_pybullet_circle")
+frame = ds[0]  # observation.state (13,), observation.environment_state (3,), action (4,)
+```
+
+## Intended use & limitations
+
+- **Intended use**: goal-conditioned, **state-based** imitation for quadrotor `circle`
+  (state + relative goal → velocity + yaw-rate). 300 episodes, all in `train`.
+- **State-based, not visuomotor.** The task is solvable from `observation.state` +
+  `observation.environment_state`; the FPV goal marker is in only **~12% of frames**.
+  The image is an **optional auxiliary** input, not the primary task signal.
+- **DART coverage, but no hard failures.** The injected noise gives off-distribution
+  recovery labels, but the data still contains no crash / large-perturbation / failure
+  demonstrations.
+- **Simulation only** (PyBullet / gym-pybullet-drones, Crazyflie 2.x). The FPV scene is a
+  synthetic benchmark scene, not photorealistic / sim-to-real imagery.
+
+
+## Data analysis
+
+Analysis across all three `ledrone` tasks, computed from this DART-regenerated data.
+
+**Signature trajectories** — takeoff climbs from the ground, hover converges & station-keeps, circle flies full loops (>= 360 deg).
+
+![Trajectories](figures/trajectories.png)
+
+**Convergence & attitude** — every task drives the goal error to near-zero (circle keeps a small steady tracking lag from following a moving target); body tilt stays gentle (median ~10 deg, 95% <= 35 deg).
+
+![Convergence and attitude](figures/convergence_attitude.png)
+
+**Command / action space** — takeoff & hover station-keep at ~0.1 m/s while circle orbits at ~1.3 m/s; the climb command is bimodal for takeoff; yaw-rate is centered on the goal bearing.
+
+![Action space](figures/action_space.png)
+
+## License & citation
+
+Apache-2.0. Please cite the [ledrone](https://github.com/ahive-org/ledrone) project.

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