--- pretty_name: "Parkour-STL" license: cc-by-4.0 # placeholder — provenance/license clearance pending (Go2 / IsaacSim EULA / Extreme-Parkour) task_categories: - reinforcement-learning - time-series-forecasting tags: - robotics - quadruped - locomotion - parkour - signal-temporal-logic - runtime-verification - formal-methods - safe-reinforcement-learning - trajectory-monitoring - neural-network-verification size_categories: - 100K **Status:** v0.1 draft card. Will be populated as the dataset is completed. ## Dataset Summary Parkour-STL is a dataset of Unitree Go2 extreme-parkour rollouts generated in IsaacSim 6.0 / IsaacLab 3.0, paired with (a) a library of **normalized, STL-monitorable predicates** sampled at 10 Hz, and (b) a **reference library of Signal Temporal Logic (STL) specifications** with ground-truth ternary (**SAT / UNK / VIOL**) labels computed by quantize-then-compute (QtC) STLCG++. 1. **Raw superset** (~75 GB) — full per-control-step simulation state (kinematics, per-body contacts, height-scan, actions, goal signals). Lets you derive your own predicates/specs. 2. **Predicates** — derived continuous, normalized signals at 10 Hz (the ready-to-use layer). 3. **Specs + labels** — a reference STL spec library and its QtC ternary labels. ## Supported Tasks & Use Cases - **Neural / learned STL monitoring** — train and benchmark learned monitors (e.g., ternary logic-gate networks, recurrent monitors) against the symbolic ground truth; study online **causal** vs **predictive** verification. - **Online runtime verification under partial observability** — bounded-future operators (e.g., `F[0,K]`) create a causal gap a predictive monitor can close but a causal one cannot. - **Failure / fall prediction & anomaly detection** — labeled crashes across competence. - **Competence-aware / safe RL** — does a monitor's verdict (and its UNK mass) track policy skill. - **Predicate / representation learning** — derive task/safety predicates from the raw state layer. - **Offline RL & imitation** (secondary) — multi-competence state–action rollouts. ## Dataset Structure ### Layers / files ``` raw_superset/ rollout_seed{S}_diff{D}_ckpt{C}_sidecar.npz # per-step sim state, ~75 GB total predicates/ rollout_seed{S}_diff{D}_ckpt{C}_pred.npz # (N, 200, P) normalized predicates specs/ spec_library.json + labels_*.npz # STL defs + QtC ternary labels videos/ (extra) chase-cam mp4 samples per (seed, ckpt, env) # qualitative inspection only ``` `S ∈ {0,1,3}` (seeds), `D ∈ {easy, hard, hard_spread}` (difficulty regimes), `C ∈ {0,300,600,900,1200,1499}` (checkpoints). ### A data instance (predicates layer) One trace = one environment's behavior from episode start to its first reset, truncated and reason-conditioned-padded to a fixed length: - `predicates`: `(200, P)` float32, ∈ [−1, 1], **boundary 0 ⇔ property satisfied** (`ρ>0 ⇔ SAT`). - 200 samples = 20 s at 10 Hz (1000 native 50 Hz control steps ÷ 5). ### Data fields **Predicates** `(N, 200, P)` float32 + `predicate_names` (column order). **Provenance / split keys** per trace: `seed_idx`, `ckpt_step`, `difficulty`, `terrain_cell_id`, `terrain_type`, `terrain_level`, `traj_type` (0=crash, 1=reach-goal success, 2=timeout). **Raw superset fields** (per native step): `projected_gravity_b`, `root_pos_w`, `root_quat_w`, `root_lin_vel_w`, `root_ang_vel_b`, `joint_pos`, `joint_vel`, `applied_torque`, per-body contact forces (base/thigh/calf/foot), `feet_in_contact`, 32-ray `height_above_terrain`, `cur_goal_idx`, `env_goals`, `env_origins`, `reset_terminated`, `reset_time_outs`, raw/processed actions. ### Splits (leakage-free) The independent unit is the **terrain course (cell)**, not the environment (same-cell envs are one course with different domain randomization). Canonical split: **partition by `terrain_cell_id`, stratified by `terrain_type`**, so every course type appears in test and no cell is shared across train/test. Provided alternatives: **checkpoint-held-out** (generalize across competence) and **seed-held-out** (cross-policy generalization). ## Predicate Library All predicates ∈ [−1, 1] with a fixed satisfaction boundary at 0. Pilot set (P = 11; to be finalized — saturated/binary signals will be regraded with saturation-aware normalization): | # | name | meaning (ρ>0 ⇔ SAT) | type | |---|---|---|---| | 1 | `track_goal_vel` | moving toward current sub-goal at ≥ commanded speed | macro / graded | | 2 | `face_goal` | oriented at the current sub-goal | macro / graded | | 3 | `progress` | course progress (`2·cur_goal_idx/8 − 1`) | macro / discrete | | 4 | `near_final_goal` | proximity to the final goal (dist→0 ⇒ +1) | macro / graded | | 5 | `upright` | body uprightness (−gravity_z) | micro / graded | | 6 | `body_clear` | no base/calf/thigh collision | micro / safety | | 7 | `attitude_calm` | low angular velocity (not tumbling) | micro / graded | | 8 | `grounded` | fraction of feet in contact (+1 all four, −1 airborne) | micro / discrete | | 9 | `episode_active` | inside the real episode vs the pad | gate | | 10 | `terminal_crash` | trace ended in a fall/collision | terminal flag | | 11 | `terminal_success` | trace ended reaching the final goal | terminal flag | The consumer thermometer-encodes each predicate (K=8 → 17 levels) before STL evaluation. ## STL Specification Library Reference specs are **composite hybrids** (macro task ∧ micro gait), with bounded-future horizons that create a causal gap and a genuine UNK band. Flagship: ``` Φ1 = G( episode_active>0 → F[0,20]( track_goal_vel>0 ∧ face_goal>0 ∧ upright>0 ∧ body_clear>0 ∧ attitude_calm>0 ) ) ``` "Never more than 2 s without a moment of simultaneously advancing-and-facing the goal *and* being upright, collision-free, and calm." Additional reference specs cover long-horizon completion (`F[0,200] near_final_goal>τ`), safety-with-recovery, and leap-and-land recovery. Each ships with QtC ternary labels; horizons are in 10 Hz samples (10–30 for windows, up to 200 for traversal-scale). ## Dataset Creation **Source.** Unitree Go2 in IsaacSim 6.0 / IsaacLab v3 (Extreme-Parkour `parkour_focused` terrain mix); PhysX 200 Hz, decimation 4 → 50 Hz control; 20 s (1000-step) episodes; 6144 parallel environments per rollout. Policies are RL (PPO) checkpoints at six training stages × seeds, giving the competence axis. Difficulty regimes are set by rescaling the terrain difficulty band. **Capture & processing.** A per-step state superset is recorded, each trace is **truncated at its first reset**, then **reason-conditioned padded** (velocities zeroed; a crash injects the failure into safety predicates), **downsampled to 10 Hz by windowed reduction** (max/min/mean per 5-step bucket), and normalized to [−1, 1] with fixed physical bounds. **Competence axis & failure distribution** (illustrative — seed 0, hard regime): | ckpt | crash % | reach-goal success % | timeout % | |---|---|---|---| | 0 | 0 | 0 | 100 | | 300 | 43 | 2 | 55 | | 600 | 55 | 10 | 34 | | 900 | 29 | 52 | 19 | | 1200 | 19 | 71 | 10 | | 1499 | 18 | 76 | 6 | Success rises monotonically with competence; crashes peak at intermediate skill — a property that holds across seeds/regimes (full table TBD). **Annotations.** Ground-truth verdicts are computed symbolically (STLCG++ QtC) over the reference specs; **no labels are baked into the predicate layer** — users may relabel with their own specs. ## Baselines / Benchmark Reference monitors to be evaluated and reported: **R-DTLGN, GR-DTLGN, BLGN, LSTM**, with **online causal STLCG++** as the symbolic baseline. Metrics: verdict accuracy, UNK calibration, the causal-vs-predictive gap, Z3 realizability/soundness, and sampling-PAC bounds. **Results: TBD** (populated when training completes — not estimated here). ## Considerations for Using the Data - **Simulation only** — IsaacSim/PhysX dynamics; a sim-to-real gap applies. - **Single robot, single task family** — Go2, extreme-parkour terrains; not a general-locomotion corpus. - **Predicates and specs are one design** — mitigated by shipping the **raw layer** so users can derive their own; STLCG++ QtC is one labeling convention. - **Known pilot caveats** being addressed before release: one safety predicate (`body_clear`) is currently near-binary (saturation-aware regrading planned); normalization constants are being finalized against full-scale value histograms. - **Sampling biases**: terrain-type proportions, checkpoint selection, and difficulty-regime mix are design choices documented above. ## Provenance, Versioning & Citation Generation stack and code are released for reproducibility (static data is the primary product; rebuilding the generator requires the exact IsaacSim/IsaacLab pins). Versioned with a changelog; v0.1 = pilot. Mirrored to a DOI archive (e.g., Zenodo) for citation. ## Datasheet (condensed) - **Why:** to give the RL ∩ formal-methods community real robot data with STL-monitorable predicates, a competence axis, and genuine failures. - **What:** time-series robot trajectories (proprioception/contacts/scan/actions/goals), derived predicates, STL specs + ternary labels. No PII. - **How collected:** simulation rollouts of RL checkpoints; fully synthetic. - **Preprocessing:** truncate-at-reset, reason-conditioned padding, 10 Hz windowed reduction, fixed-bound normalization (all documented + code released). - **Recommended uses / out-of-scope:** monitoring/verification/failure-prediction research; **not** a sim-to-real-validated control benchmark. - **Maintenance:** versioned releases, issue tracker, changelog; raw + derived layers kept in sync.