On the Continuity of Rotation Representations in Neural Networks
Paper • 1812.07035 • Published • 2
Datasets:
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observation.state list | observation.environment_state list | action list | timestamp float32 0 6.63 | frame_index int64 0 199 | episode_index int64 0 299 | index int64 0 60k | task_index int64 0 0 |
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End of preview. Expand in Data Studio
ledrone_pybullet_hover
A goal-conditioned, state-based imitation-learning dataset of a quadrotor performing the hover task (reach a fixed target position and station-keep), 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, ACT, …). A first-person-view (FPV) camera is shipped as an optional auxiliary stream (the task is solvable from state alone).
Built with LeRobot (format v3.0) as part
of the ledrone project.
At a glance
| Task | hover — reach a fixed target position and station-keep |
| Type | Goal-conditioned, state-based (FPV image is an optional auxiliary) |
| Episodes | 300 |
| Frames | 60,000 (200/episode, 6.7s at 30 Hz) |
| Robot type | ledrone (quadrotor) |
| Physics | PyBullet rigid-body dynamics via 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 hover reference in the LeDrone env on the PyBullet backend.
- DART-style coverage (Laskey et al. 2017): 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):
python tools/generate_expert_dataset.py --task hover --backend pybullet \
--repo-id ahive/ledrone_pybullet_hover --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) 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
from lerobot.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("ahive/ledrone_pybullet_hover")
frame = ds[0] # observation.state (13,), observation.environment_state (3,), action (4,)
Intended use & limitations
- Intended use: goal-conditioned, state-based imitation for quadrotor
hover(state + relative goal → velocity + yaw-rate). 300 episodes, all intrain. - State-based, not visuomotor. The task is solvable from
observation.state+observation.environment_state; the FPV goal marker is in only ~7% 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).
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).
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.
License & citation
Apache-2.0. Please cite the ledrone project.
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