Simulation & data generation

{
  "proximity": { "left": 0.32, "right": 0.76 },
  "collision": false,
  "collision_count": 1,
  "done": false,
  "t_ms": 41230,
  "sensor_age_ms": 770,
  "applied_action": "left",
  "labels": [{ "id": "obj_12", "type": "wall", "confidence": 1 }],
  "camera": { "image": "data:image/jpeg;base64,..." }
}

• proximity — two virtual range sensors (like the e-puck’s IR pair). 0 = clear, 1.0 = at contact range. Objects left of the robot’s centre excite the left sensor, right excites right; something dead ahead (a wall) excites both. Compact obstacles grade by screen-space closeness, walls by time-to-contact.
• collision — a spike: true exactly once per confirmed collision, and guaranteed to read 1.0 proximity on the colliding side. The monotonic collision_count means a spike can never be missed between reads.
• sensor_age_ms — how far the sensors lag the camera frame in the same observation (cloud-detector worlds ≈ 0.5–1 s, local-detector worlds tens of ms). Compensate or discount accordingly.
• camera — optional square RGB frame (64/128/256), requested per session.

​

The control loop

import requests

GYM = "https://gym.alakazam.gg"   # bearer token issued to you

H = {"Authorization": "Bearer <your-token>"}

sid = requests.post(f"{GYM}/v1/sim/sessions", headers=H, json={
    "world": "epuck",
    "camera": 128,
    "terminal_collision": False,   # collisions = spikes, run continues
}).json()["session_id"]

obs = requests.get(f"{GYM}/v1/sim/sessions/{sid}/obs", headers=H).json()
while not obs["done"]:
    v_l, v_r = my_controller.step(obs["proximity"], obs.get("camera"))
    obs = requests.post(f"{GYM}/v1/sim/sessions/{sid}/step", headers=H, json={
        "wheels": {"left": v_l, "right": v_r},   # or {"action": "left"}
        "holdMs": 300,
    }).json()

requests.post(f"{GYM}/v1/sim/sessions/{sid}/reset", headers=H)   # next episode (new seed)

​

Generating a dataset

node scripts/robot-gauntlet/generate_snn_dataset.mjs \
  --worlds epuck,robot --episodes 10 --seconds 30 --camera 64

python scripts/robot-gauntlet/snn_jsonl_to_npz.py \
  datasets/robot_gauntlet/snn/dataset_<stamp>.jsonl --camera

​

Credits & rate limits

• Cost of an episode ≈ steps × holdMs of live seconds (plus ~30–60 s of world boot per session). Example: 100 steps at 600 ms = 60 s — the free developer tier’s 7,200 session_seconds/day covers about 120 such episodes.
• Steps, observations, resets and dataset files are free. reset ends the old stream before booting the new one, so seconds never double-count.
• No per-minute rate limit applies to the control loop. Like the other runtime routes, step/obs sit outside the per-key 60-second limiter (see rate limits); they’re bounded by your daily quota, the live-session concurrency cap (admission queues FIFO when capacity is full), and the holdMs floor of 60 ms (≈16 steps/s per session).
• Exhausting the daily quota returns 402 at session creation, like any other session mint.

​

Budgeting

• One session = one live GPU stream. Generate sequentially, or cap parallel sessions to your quota.
• reset reuses the gym session — it is the cheap way to get new episode seeds.
• Delete sessions when done; abandoned ones fall to the world’s idle governor.