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153352c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | #!/usr/bin/env python3
"""Verify the Phase-1 TartanDrive MuJoCo environment end-to-end.
Builds a TartanDriveEnv for one sequence, runs a scripted rollout, saves a
few rendered camera frames plus a top-down overlay of the sim trajectory vs.
the real GPS track (on the Gascola orthophoto), and optionally runs a fast
reset/step smoke-test. Success is qualitative for this PoC: the env should
reset/step without exceptions, observations must match observation_space,
and the vehicle should drive a plausible distance without tunneling through
terrain or diverging (NaNs) -- it is not expected to numerically retrace the
real GPS track.
"""
import argparse
import os
import subprocess
import sys
os.environ.setdefault("MUJOCO_GL", "egl")
from pathlib import Path
REPO_ROOT = Path(__file__).resolve().parent.parent
if str(REPO_ROOT) not in sys.path:
sys.path.insert(0, str(REPO_ROOT))
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from metadata_utils.lidar_terrain import load_sequence_poses
from sim.env import TartanDriveEnv
def try_pull_atv_scan():
scan_path = REPO_ROOT / "assets" / "vicky1_clean.pts"
if scan_path.exists():
return
print("[verify_sim_env] assets/vicky1_clean.pts not found; attempting download "
"(falls back to generic UTV dimensions in sim/vehicle_config.py if unreachable)...")
try:
subprocess.run([sys.executable, str(REPO_ROOT / "scripts" / "pull_atv_scan.py")],
cwd=REPO_ROOT, timeout=20, capture_output=True, check=True)
print("[verify_sim_env] fetched vicky1_clean.pts")
except Exception as e:
print(f"[verify_sim_env] could not fetch vicky1_clean.pts ({e}); "
f"using generic UTV approximation")
def run_smoke_test(env):
obs, info = env.reset()
assert env.observation_space.contains(obs), "reset() obs violates observation_space"
assert env.action_space.shape == (2,)
for _ in range(20):
action = env.action_space.sample()
obs, reward, terminated, truncated, info = env.step(action)
assert env.observation_space.contains(obs), "step() obs violates observation_space"
assert np.isfinite(obs["odom"]).all(), "odom contains NaN/inf"
assert np.isfinite(obs["imu"]).all(), "imu contains NaN/inf"
if terminated or truncated:
obs, info = env.reset()
print("[smoke-test] OK: reset/step observations match observation_space, no NaNs")
def rollout(env, steps, frame_every=50):
obs, info = env.reset()
xy = [env.data.qpos[env._free_qpos_adr:env._free_qpos_adr + 2].copy()]
frames = [(0, obs["image"].copy())]
ended_at = None
for i in range(1, steps + 1):
throttle, steer = 0.35, 0.25 * np.sin(i / 60.0)
obs, reward, terminated, truncated, info = env.step(
np.array([throttle, steer], dtype=np.float32))
xy.append(env.data.qpos[env._free_qpos_adr:env._free_qpos_adr + 2].copy())
if i % frame_every == 0:
frames.append((i, obs["image"].copy()))
if terminated or truncated:
ended_at = i
print(f"[rollout] episode ended at step {i} "
f"(terminated={terminated}, truncated={truncated})")
break
return np.array(xy), frames, ended_at
def save_frames(frames, out_dir):
out_dir.mkdir(parents=True, exist_ok=True)
for i, img in frames:
Image.fromarray(img).save(out_dir / f"frame_{i:05d}.png")
def save_overlay(env, sim_xy, out_path):
poses = load_sequence_poses(env.scene_dir)
real_xy = poses.positions[:, :2] # (easting, northing), relative to sequence start
fig, ax = plt.subplots(figsize=(8, 8))
try:
import rasterio
with rasterio.open(REPO_ROOT / "assets" / "gascola.tif") as ds:
img = ds.read([1, 2, 3]).transpose(1, 2, 0)
left, top = ds.transform * (0, 0)
right, bottom = ds.transform * (ds.width, ds.height)
utm0 = env.terrain.utm_origin
extent = [left - utm0[0], right - utm0[0], bottom - utm0[1], top - utm0[1]]
ax.imshow(img, extent=extent)
except Exception as e:
print(f"[overlay] could not load gascola.tif basemap ({e}); plotting trajectories only")
ax.plot(real_xy[:, 0], real_xy[:, 1], "-", color="0.6", lw=1.0, label="real GPS track (full)")
ax.plot(sim_xy[:, 0], sim_xy[:, 1], "-", color="tab:red", lw=2.0, label="sim rollout")
ax.plot(0, 0, "k*", ms=14, label="sequence start")
ax.set_xlabel("relative easting [m]")
ax.set_ylabel("relative northing [m]")
ax.legend(loc="best")
ax.set_aspect("equal")
margin = 15
ax.set_xlim(sim_xy[:, 0].min() - margin, sim_xy[:, 0].max() + margin)
ax.set_ylim(sim_xy[:, 1].min() - margin, sim_xy[:, 1].max() + margin)
fig.tight_layout()
fig.savefig(out_path, dpi=150)
plt.close(fig)
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--sequence", default="2023-10-26-14-42-35_turnpike_afternoon_fall")
parser.add_argument("--kitti-root", default=str(REPO_ROOT / "kitti_format"))
parser.add_argument("--steps", type=int, default=350)
parser.add_argument("--frame-stride", type=int, default=3,
help="use every Nth lidar frame when aggregating terrain")
parser.add_argument("--frame-range-end", type=int, default=300,
help="restrict terrain aggregation to frames [0, this) for a fast PoC build")
parser.add_argument("--cell-size", type=float, default=0.3)
parser.add_argument("--out", default=str(REPO_ROOT / "sim_verify_out"))
parser.add_argument("--smoke-test", action="store_true")
args = parser.parse_args()
try_pull_atv_scan()
scene_dir = Path(args.kitti_root) / args.sequence
out_dir = Path(args.out)
out_dir.mkdir(parents=True, exist_ok=True)
cache_path = out_dir / f"{args.sequence}_terrain.npz"
env = TartanDriveEnv(
scene_dir=scene_dir, cache_path=cache_path,
frame_stride=args.frame_stride, cell_size=args.cell_size,
frame_range=(0, args.frame_range_end), max_episode_steps=args.steps + 1)
if args.smoke_test:
run_smoke_test(env)
sim_xy, frames, ended_at = rollout(env, args.steps)
save_frames(frames, out_dir / "frames")
save_overlay(env, sim_xy, out_dir / "trajectory_overlay.png")
env.close()
distance = float(np.linalg.norm(sim_xy[-1] - sim_xy[0]))
status = "SUCCESS" if ended_at is None else "ENDED EARLY (tilt/step limit)"
print(f"[verify_sim_env] {status}: {len(sim_xy) - 1} steps, "
f"net displacement {distance:.2f} m, {len(frames)} frames saved to {out_dir}")
if __name__ == "__main__":
main()
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