results: jobs/20261011-002225-large-ilqr-steer-learn/** index/episodes.jsonl leaderboard/*

results/jobs/20261011-002225-large-ilqr-steer-learn/breakdown.json

@@ -0,0 +1,460 @@
+{
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+ }
+}

results/jobs/20261011-002225-large-ilqr-steer-learn/episodes.jsonl

@@ -0,0 +1,45 @@
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+{"key": "84a97a0bd6a81d7b91e27c5c8f7b392b8331b3147ae0242f6c24ccaa82dcac5b", "kart_name": "kart1", "condition": "nominal", "scenario": {"type": "lap", "line": "line1", "laps": 1}, "status": "ok", "cause": null, "context_valid": null, "laps_completed": 1, "laps_attempted": 1, "wall_s": 63.77969587384723, "sim_s": 61.0, "t_end": 61.0, "handoff_t": 8.1, "auto_s": 52.371000000001985, "cte_hist": [16, 3, 0, 3, 1, 0, 1, 2, 5, 1, 0, 3, 1, 4, 4, 2, 3, 6, 6, 4, 9, 8, 8, 5, 6, 5, 8, 4, 9, 11, 7, 8, 10, 16, 15, 25, 10, 26, 14, 17, 20, 21, 23, 25, 25, 74, 30, 34, 46, 45, 46, 76, 56, 55, 52, 37, 31, 25, 34, 18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "cte_mean": 0.3051999665940459, "off_track_pct": 13.201008191556395, "safety": 0.7432262129804663, "events": 62, "osc_10": 51.356993736951985, "osc_20": 25.052192066805844, "osc_30": 10.020876826722338, "peak_cte_after_m": 0.7066483342577319, "unsafe_lat": 0.0, "unsafe_rate": 0.13705103969754254, "unsafe_travel": 0.0, "unsafe_spin": 0.0, "unsafe_off": 0.13201008191556396, "cte_p50": 0.301384942866627, "cte_p90": 0.5514724674996493, "cte_p95": 0.6281749220499795, "cte_p99": 0.6871279348272599, "recovered": null, "recovery_s": null, "loc_n": 3174, "loc_ss": 725.0143827064551}
+{"key": "2457c2353264a3ce38fa49aead4d981b187ca1806d07904947a50acb7d1c9deb", "kart_name": "kart2", "condition": "nominal", "scenario": {"type": "lap", "line": "line1", "laps": 1}, "status": "ok", "cause": null, "context_valid": null, "laps_completed": 1, "laps_attempted": 1, "wall_s": 63.138601794838905, "sim_s": 60.417, "t_end": 60.417, "handoff_t": 8.1, "auto_s": 51.79350000000196, "cte_hist": [14, 1, 1, 4, 0, 2, 2, 2, 1, 0, 3, 1, 3, 2, 4, 7, 5, 7, 4, 9, 6, 2, 4, 5, 7, 6, 5, 10, 8, 8, 6, 12, 11, 11, 21, 10, 22, 17, 27, 27, 21, 25, 23, 31, 31, 31, 40, 53, 45, 40, 50, 59, 43, 43, 43, 43, 53, 40, 36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "cte_mean": 0.3028234335930769, "off_track_pct": 15.673781459063395, "safety": 0.6244026760114686, "events": 89, "osc_10": 49.471458773784356, "osc_20": 30.443974630021142, "osc_30": 11.416490486257928, "peak_cte_after_m": 0.6762452288279992, "unsafe_lat": 0.0, "unsafe_rate": 0.28735266008282895, "unsafe_travel": 0.0, "unsafe_spin": 0.0, "unsafe_off": 0.15673781459063396, "cte_p50": 0.2945860983490319, "cte_p90": 0.5607838252509767, "cte_p95": 0.6113293558314502, "cte_p99": 0.6514292751462675, "recovered": null, "recovery_s": null, "loc_n": 3139, "loc_ss": 727.2167979404151}
+{"key": "32d694d03688ccf3eb2cb30c41e4f104d7961e12513e937dae2a79225132170a", "kart_name": "kart3", "condition": "nominal", "scenario": {"type": "lap", "line": "line1", "laps": 1}, "status": "ok", "cause": null, "context_valid": null, "laps_completed": 1, "laps_attempted": 1, "wall_s": 63.34387627406977, "sim_s": 61.117000000000004, "t_end": 61.117000000000004, "handoff_t": 8.1, "auto_s": 52.48650000000199, "cte_hist": [16, 2, 0, 1, 1, 1, 1, 1, 0, 6, 3, 4, 1, 2, 3, 3, 2, 6, 5, 4, 6, 5, 2, 6, 5, 9, 10, 7, 8, 10, 12, 14, 3, 20, 17, 24, 17, 13, 30, 19, 28, 28, 43, 29, 42, 27, 41, 39, 65, 55, 56, 76, 53, 55, 39, 36, 35, 8, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "cte_mean": 0.27902259103870225, "off_track_pct": 12.417478780257781, "safety": 0.7195850361521534, "events": 83, "osc_10": 53.75, "osc_20": 32.5, "osc_30": 20.0, "peak_cte_after_m": 0.6585955400203395, "unsafe_lat": 0.0, "unsafe_rate": 0.18327569946557687, "unsafe_travel": 0.0, "unsafe_spin": 0.0, "unsafe_off": 0.1241747878025778, "cte_p50": 0.2805122404553884, "cte_p90": 0.4923807322405134, "cte_p95": 0.5425945278836486, "cte_p99": 0.5965019871508933, "recovered": null, "recovery_s": null, "loc_n": 3181, "loc_ss": 728.4847618603451}

results/jobs/20261011-002225-large-ilqr-steer-learn/gautschi/0/done.txt

@@ -0,0 +1,45 @@
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results/jobs/20261011-002225-large-ilqr-steer-learn/gautschi/0/gautschi_launch.py

@@ -0,0 +1,145 @@
+"""Runs on a Gautschi node (stdlib only): episodes from a queue in parallel workers, one contained Apptainer per episode."""
+import argparse
+import json
+import os
+import queue
+import shutil
+import subprocess
+import threading
+import time
+
+RUN = ("source /opt/ros/humble/setup.bash && source /ws/install/setup.bash"
+       " && python3 -m simnode.batch /batch.json /out")
+CUDA_LIB = "/ws/mpc_cuda/libmpc_cuda.so"   # built per stack by runner/gautschi.build_stack for gautschi-gpu
+SAMPLE_S = 15
+STAGGER_S = 0.25   # between worker starts: the dev job's one error was a stack starved in the 96-way start burst
+EPISODE_TIMEOUT_S = 1200   # about 4x the slowest lap; a killed episode has no outcome and is retried by the host
+
+
+def worker_cmd(i, work, out, ep, sif, engine, stack, gpu=None):
+    """Private /tmp, HOME, /dev/shm, IPC, PIDs and loopback per episode, plus a distinct ROS_DOMAIN_ID per worker;
+    gpu (a host CUDA device) adds the driver and the stack's CUDA MPC kernel."""
+    cuda = ["--nv", "--env", f"CUDA_VISIBLE_DEVICES={gpu}", "--env", f"MPC_CUDA_LIB={CUDA_LIB}"] if gpu is not None else []
+    if gpu is None and os.path.exists(os.path.join(stack, "mpc_cuda", "libmpc_cuda.so")):
+        cuda = ["--env", f"MPC_CUDA_LIB={CUDA_LIB}"]   # CPU node: the library's native iLQR solve still loads; its MPPI falls back to numpy
+    return ["apptainer", "exec", *cuda, "--contain", "--cleanenv", "--ipc", "--pid", "--net", "--network", "none", "--workdir", work,
+            "--env", f"ROS_DOMAIN_ID={i + 1}", "--env", "ROS_HOME=/tmp/ros", "--env", "TMPDIR=/tmp",
+            "--bind", f"{engine}:/opt/sim:ro", "--bind", f"{stack}:/ws:ro", "--bind", f"{out}:/out",
+            "--bind", f"{ep}:/batch.json:ro", sif, "bash", "-lc", RUN]
+
+
+def parse_smi(text):
+    """nvidia-smi 'utilization.gpu, memory.used' csv rows to per-GPU lists."""
+    rows = [[int(v) for v in ln.split(",")] for ln in text.strip().splitlines() if ln.strip()]
+    return {"gpu_util": [r[0] for r in rows], "gpu_mem_mib": [r[1] for r in rows]}
+
+
+def _solver(logdir):
+    """pathfinder's 'MPC solver: ...' and CUDA fallback lines from the episode's ROS logs."""
+    lines = []
+    for dp, _, fns in os.walk(logdir):
+        for fn in fns:
+            try:
+                with open(os.path.join(dp, fn), errors="replace") as f:
+                    lines += [ln.strip() for ln in f if "MPC solver:" in ln or "CUDA MPC unavailable" in ln]
+            except OSError:
+                pass
+    return "\n".join(dict.fromkeys(lines))
+
+
+def main(argv=None):
+    p = argparse.ArgumentParser()
+    for a in ("episodes", "out", "sif", "engine", "stack", "work", "timeline", "done"):
+        p.add_argument("--" + a, required=True)
+    p.add_argument("--workers", type=int, required=True)
+    p.add_argument("--gpus", type=int, default=0)
+    a = p.parse_args(argv)
+    with open(a.episodes) as f:
+        q = queue.Queue()
+        for e in json.load(f):   # the host already ordered them, longest first
+            q.put(e)
+    os.makedirs(a.out, exist_ok=True)
+    lock, t0 = threading.Lock(), time.time()
+    devs = [d for d in os.environ.get("CUDA_VISIBLE_DEVICES", "").split(",") if d][:a.gpus] or [str(n) for n in range(a.gpus)]
+
+    def log(**ev):
+        with lock, open(a.timeline, "a") as f:
+            f.write(json.dumps(dict(ev, t=round(time.time() - t0, 3))) + "\n")
+
+    def worker(i):
+        time.sleep(i * STAGGER_S)
+        while True:
+            try:
+                e = q.get_nowait()
+            except queue.Empty:
+                return
+            key, work = e["key"], os.path.join(a.work, e["key"])
+            stage, ep = os.path.join(work, "stage"), os.path.join(work, "ep.json")
+            os.makedirs(stage)
+            with open(ep, "w") as f:
+                json.dump([e], f)
+            log(ev="start", key=key, worker=i)
+            gpu = devs[i % len(devs)] if devs and e["resolved"].get("recipe", {}).get("sim", {}).get("cuda") else None
+            limit = EPISODE_TIMEOUT_S * max(1, e["resolved"]["scenario"].get("laps", 1))   # per lap: 5-lap episodes run 5x longer
+            try:
+                rc = subprocess.run(worker_cmd(i, work, stage, ep, a.sif, a.engine, a.stack, gpu), stdout=subprocess.DEVNULL,
+                                    stderr=subprocess.DEVNULL, timeout=limit).returncode
+            except subprocess.TimeoutExpired:
+                rc = "timeout"
+            solver = _solver(os.path.join(work, "tmp", "ros", "log"))
+            if solver and os.path.isdir(os.path.join(stage, key)):
+                with open(os.path.join(stage, key, "solver.txt"), "w") as f:
+                    f.write(solver + "\n")
+            if os.path.exists(os.path.join(stage, key, "outcome.json")):
+                tmp, dst = os.path.join(a.out, ".tmp-" + key), os.path.join(a.out, key)
+                shutil.rmtree(tmp, ignore_errors=True)
+                shutil.copytree(os.path.join(stage, key), tmp)
+                shutil.rmtree(dst, ignore_errors=True)
+                os.rename(tmp, dst)   # the host's rsync sees a key only once its directory is complete
+                with lock, open(a.done, "a") as f:
+                    f.write(key + "\n")
+            shutil.rmtree(work, ignore_errors=True)
+            log(ev="end", key=key, worker=i, rc=rc, gpu=gpu,
+                solver=solver.split("MPC solver: ")[-1] if "MPC solver: " in solver else solver or None)
+
+    def sampler(stop):
+        """Job CPU use from its cgroup, as busy cores, every SAMPLE_S."""
+        try:
+            with open("/proc/self/cgroup") as f:
+                path = "/sys/fs/cgroup" + f.read().strip().split(":")[-1] + "/cpu.stat"
+            prev = None
+            while not stop.wait(SAMPLE_S if prev else 0):
+                with open(path) as f:
+                    use = next(int(ln.split()[1]) for ln in f if ln.startswith("usage_usec"))
+                now = time.time()
+                if prev:
+                    log(ev="cpu", busy_cores=round((use - prev[0]) / 1e6 / (now - prev[1]), 2))
+                prev = (use, now)
+        except (OSError, StopIteration, ValueError):
+            pass
+
+    def gpu_sampler(stop):
+        """GPU utilisation and memory of the job's GPUs, every SAMPLE_S."""
+        while not stop.wait(SAMPLE_S):
+            try:
+                p = subprocess.Popen(["nvidia-smi", "--query-gpu=utilization.gpu,memory.used", "--format=csv,noheader,nounits"],
+                                     stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, text=True)
+                log(ev="gpu", **parse_smi(p.communicate(timeout=30)[0]))
+            except Exception:   # no driver or a hung query: skip this sample
+                pass
+
+    cores = len(os.sched_getaffinity(0)) if hasattr(os, "sched_getaffinity") else os.cpu_count()
+    log(ev="launch", workers=a.workers, cores=cores)
+    stop = threading.Event()
+    threads = [threading.Thread(target=worker, args=(i,)) for i in range(a.workers)]
+    samplers = [sampler] + ([gpu_sampler] if a.gpus else [])
+    for t in threads + [threading.Thread(target=f, args=(stop,), daemon=True) for f in samplers]:
+        t.start()
+    for t in threads:
+        t.join()
+    stop.set()
+    log(ev="done")
+
+
+if __name__ == "__main__":
+    main()

results/jobs/20261011-002225-large-ilqr-steer-learn/gautschi/0/sbatch.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+#SBATCH -p highmem -A lilly-rob1 -N 1 -n 1 -c 96 --time 11 -J AutoSim-smoke --comment 20261011-002225-large-ilqr-steer-learn -o AutoSim/jobs/20261011-002225-large-ilqr-steer-learn/0/slurm-%j.out
+W=/dev/shm/AutoSim-$SLURM_JOB_ID
+trap 'rm -rf "$W"' EXIT
+python3 $HOME/AutoSim/jobs/20261011-002225-large-ilqr-steer-learn/0/gautschi_launch.py --episodes $HOME/AutoSim/jobs/20261011-002225-large-ilqr-steer-learn/0/episodes.json --out /scratch/gautschi/manors/AutoSim/out/20261011-002225-large-ilqr-steer-learn --sif $HOME/AutoSim/images/AutoSim-99d206f40cb1.sif --engine $HOME/AutoSim/engine/b37fb519b32e564d2d8a8ea1259cbf2e695ba1a28d93504f20e4c2970a4f6f00 --stack $HOME/AutoSim/stacks/09f6048d97a12a43c7f0539e9db22b0251c622d6cafda2946841254a27f32824 --workers 45 --work $W --timeline $HOME/AutoSim/jobs/20261011-002225-large-ilqr-steer-learn/0/timeline.jsonl --done $HOME/AutoSim/jobs/20261011-002225-large-ilqr-steer-learn/0/done.txt

results/jobs/20261011-002225-large-ilqr-steer-learn/job.json

@@ -0,0 +1,130 @@
+{
+ "id": "20261011-002225-large-ilqr-steer-learn",
+ "tier": "smoke",
+ "tier_version": 3,
+ "recipe": {
+  "name": "large-ilqr-steer-learn",
+  "source": {
+   "repo": "AutonomousKart",
+   "ref": "worktree:ilqr-steer-learn"
+  },
+  "params": {
+   "pathfinder_node": {
+    "planner": "ilqr",
+    "mpc.num_samples": 16,
+    "mpc.use_steer_map": true,
+    "mpc.steer_map_cmd_deg": [
+     -60.0,
+     -30.0,
+     -18.0,
+     -15.0,
+     -12.0,
+     -9.0,
+     -6.0,
+     -3.0,
+     0.0,
+     3.0,
+     6.0,
+     9.0,
+     12.0,
+     15.0,
+     18.0,
+     30.0,
+     60.0
+    ],
+    "mpc.adaptive_gain.map": true,
+    "mpc.adaptive_gain.understeer": false
+   }
+  },
+  "sim": {},
+  "notes": "large-ilqr-fast with a learned steer map: the rollout starts from each kart design's scalar gain (small-angle slope of the steer-map fit: 0.635, 0.357, 0.537) and RLS on IMU yaw rate learns the 17 knots, used as learned; no separate K_us (the starting gain is measured at driving speed, so K_us on top double counts understeer; the map absorbs it as in the predefined map)",
+  "params_by_kart": {
+   "kart1": {
+    "pathfinder_node": {
+     "mpc.steer_map_wheel_deg": [
+      -38.112,
+      -19.056,
+      -11.4336,
+      -9.528,
+      -7.6224,
+      -5.7168,
+      -3.8112,
+      -1.9056,
+      0.0,
+      1.9056,
+      3.8112,
+      5.7168,
+      7.6224,
+      9.528,
+      11.4336,
+      19.056,
+      38.112
+     ]
+    }
+   },
+   "kart2": {
+    "pathfinder_node": {
+     "mpc.steer_map_wheel_deg": [
+      -21.408,
+      -10.704,
+      -6.4224,
+      -5.352,
+      -4.2816,
+      -3.2112,
+      -2.1408,
+      -1.0704,
+      0.0,
+      1.0704,
+      2.1408,
+      3.2112,
+      4.2816,
+      5.352,
+      6.4224,
+      10.704,
+      21.408
+     ]
+    }
+   },
+   "kart3": {
+    "pathfinder_node": {
+     "mpc.steer_map_wheel_deg": [
+      -32.19,
+      -16.095,
+      -9.657,
+      -8.0475,
+      -6.438,
+      -4.8285,
+      -3.219,
+      -1.6095,
+      0.0,
+      1.6095,
+      3.219,
+      4.8285,
+      6.438,
+      8.0475,
+      9.657,
+      16.095,
+      32.19
+     ]
+    }
+   }
+  }
+ },
+ "user": "shay",
+ "notes": "smoke: large-ilqr-steer-learn without K_us (pre-large check)",
+ "where": "gautschi",
+ "status": "done",
+ "submitted_at": "2026-10-11T07:22:25.525+00:00",
+ "started_at": "2026-10-11T07:22:25.527+00:00",
+ "finished_at": "2026-10-11T07:24:34.562+00:00",
+ "total": 45,
+ "done": 45,
+ "errors": 0,
+ "build_log": null,
+ "workers": 45,
+ "slurm_id": "16948078",
+ "slurm_ids": [
+  "16948078"
+ ],
+ "engine_version": "b37fb519b32e564d2d8a8ea1259cbf2e695ba1a28d93504f20e4c2970a4f6f00"
+}

results/jobs/20261011-002225-large-ilqr-steer-learn/summary.json

@@ -0,0 +1,156 @@
+{
+ "cte_p50": {
+  "value": 0.2541707976543653,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.2570003085668135,
+   "kart2": 0.25307721425745033,
+   "kart3": 0.25199863374019554
+  }
+ },
+ "cte_p90": {
+  "value": 0.5456679982798828,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.533781293173816,
+   "kart2": 0.575343531581307,
+   "kart3": 0.5397712045936794
+  }
+ },
+ "cte_p99": {
+  "value": 1.5742101700754108,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 1.5100490000565103,
+   "kart2": 1.6405141162731458,
+   "kart3": 1.5590822928866397
+  }
+ },
+ "recovery_rate": {
+  "value": 1.0,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 1.0,
+   "kart2": 1.0,
+   "kart3": 1.0
+  }
+ },
+ "recovery_s_median": {
+  "value": 4.91675,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 3.92525,
+   "kart2": 5.2917499999999995,
+   "kart3": 3.7249999999999996
+  }
+ },
+ "safety": {
+  "value": 0.7697832520195154,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.7950443134889332,
+   "kart2": 0.7498068702683215,
+   "kart3": 0.7625191441964078
+  }
+ },
+ "unsafe_lat": {
+  "value": 0.0,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.0,
+   "kart2": 0.0,
+   "kart3": 0.0
+  }
+ },
+ "unsafe_rate": {
+  "value": 0.14849236183316003,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.11563268525822468,
+   "kart2": 0.1877736004532111,
+   "kart3": 0.14595999443077923
+  }
+ },
+ "unsafe_travel": {
+  "value": 0.0,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.0,
+   "kart2": 0.0,
+   "kart3": 0.0
+  }
+ },
+ "unsafe_spin": {
+  "value": 0.0,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.0,
+   "kart2": 0.0,
+   "kart3": 0.0
+  }
+ },
+ "unsafe_off": {
+  "value": 0.10763816683995842,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.1110853324671709,
+   "kart2": 0.09270227120564453,
+   "kart3": 0.11764978883371234
+  }
+ },
+ "osc_10": {
+  "value": 37.93218413781203,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 37.41521875451199,
+   "kart2": 37.08672681147647,
+   "kart3": 39.21112625880134
+  }
+ },
+ "osc_20": {
+  "value": 17.651927773569778,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 16.094337245637128,
+   "kart2": 14.030321362738928,
+   "kart3": 22.47340478413176
+  }
+ },
+ "osc_30": {
+  "value": 6.17579003479415,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 3.9577818350441563,
+   "kart2": 3.8361129833267844,
+   "kart3": 10.502601378422215
+  }
+ },
+ "laps_pct": {
+  "value": 88.88888888888889,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 100.0,
+   "kart2": 66.66666666666667,
+   "kart3": 100.0
+  }
+ },
+ "failures": {
+  "value": 1,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0,
+   "kart2": 1,
+   "kart3": 0
+  }
+ },
+ "n": {
+  "value": 45,
+  "worst_kart": null,
+  "per_kart": {}
+ },
+ "errors": {
+  "value": 0,
+  "worst_kart": null,
+  "per_kart": {}
+ }
+}

results/leaderboard/smoke-3__b37fb519b32e.json

@@ -1,50 +1,4 @@
 [
- {
-  "job_id": "20261011-001500-large-ilqr-steer-learn",
-  "recipe": "large-ilqr-steer-learn",
-  "notes": "smoke: large-ilqr-steer-learn (pre-large check)",
-  "user": "shay",
-  "ran_at": "2026-10-11T07:17:06.311+00:00",
-  "duration_s": 125.461,
-  "cte_p50": 0.25273829378319984,
-  "cte_p90": 0.5883013241806034,
-  "cte_p99": 1.61783519595897,
-  "recovery_rate": 1.0,
-  "recovery_s_median": 5.58325,
-  "safety": 0.7265821020351269,
-  "unsafe_lat": 0.00024393643713409535,
-  "unsafe_rate": 0.18898104265402846,
-  "unsafe_travel": 0.0,
-  "unsafe_spin": 1.7424031223863953e-05,
-  "unsafe_off": 0.1273173961527739,
-  "osc_10": 43.20111041636124,
-  "osc_20": 20.66069414639829,
-  "osc_30": 9.531799498852362,
-  "laps_pct": 55.55555555555556,
-  "failures": 4,
-  "n": 45,
-  "errors": 0,
-  "worst": {
-   "cte_p50": 0.26657334376092234,
-   "cte_p90": 0.6017772095575997,
-   "cte_p99": 1.6682736685107382,
-   "recovery_rate": 1.0,
-   "recovery_s_median": 5.83325,
-   "safety": 0.7161027859313044,
-   "unsafe_lat": 0.0007528905619790266,
-   "unsafe_rate": 0.20858705748787285,
-   "unsafe_travel": 0.0,
-   "unsafe_spin": 5.3777897284216184e-05,
-   "unsafe_off": 0.146275880613068,
-   "osc_10": 47.13689689931536,
-   "osc_20": 26.991677178716976,
-   "osc_30": 15.098266686443084,
-   "laps_pct": 33.333333333333336,
-   "failures": 2,
-   "n": 45,
-   "errors": 0
-  }
- },
  {
   "job_id": "20261011-001459-large-ilqr-steer-map",
   "recipe": "large-ilqr-steer-map",
@@ -90,5 +44,51 @@
    "n": 45,
    "errors": 1
   }
+ },
+ {
+  "job_id": "20261011-002225-large-ilqr-steer-learn",
+  "recipe": "large-ilqr-steer-learn",
+  "notes": "smoke: large-ilqr-steer-learn without K_us (pre-large check)",
+  "user": "shay",
+  "ran_at": "2026-10-11T07:24:34.562+00:00",
+  "duration_s": 129.035,
+  "cte_p50": 0.2541707976543653,
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+  "cte_p99": 1.5742101700754108,
+  "recovery_rate": 1.0,
+  "recovery_s_median": 4.91675,
+  "safety": 0.7697832520195154,
+  "unsafe_lat": 0.0,
+  "unsafe_rate": 0.14849236183316003,
+  "unsafe_travel": 0.0,
+  "unsafe_spin": 0.0,
+  "unsafe_off": 0.10763816683995842,
+  "osc_10": 37.93218413781203,
+  "osc_20": 17.651927773569778,
+  "osc_30": 6.17579003479415,
+  "laps_pct": 88.88888888888889,
+  "failures": 1,
+  "n": 45,
+  "errors": 0,
+  "worst": {
+   "cte_p50": 0.2570003085668135,
+   "cte_p90": 0.575343531581307,
+   "cte_p99": 1.6405141162731458,
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+   "recovery_s_median": 5.2917499999999995,
+   "safety": 0.7498068702683215,
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+   "unsafe_rate": 0.1877736004532111,
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+   "osc_20": 22.47340478413176,
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+   "laps_pct": 66.66666666666667,
+   "failures": 1,
+   "n": 45,
+   "errors": 0
+  }
  }
 ]