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results: jobs/20261105-024128-A26_MPPI___Adaptive_Gain/** index/episodes.jsonl leaderboard/*

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+ },
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+ {
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+ }
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+ }
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+ }
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+ ],
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+ },
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+ "clips": {
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+ "recovery_s_median": null
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+ }
1883
+ }
1884
+ }
results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/episodes.jsonl ADDED
The diff for this file is too large to render. See raw diff
 
results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/gautschi/0/done.txt ADDED
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results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/gautschi/0/episodes.json ADDED
The diff for this file is too large to render. See raw diff
 
results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/gautschi/0/gautschi_launch.py ADDED
@@ -0,0 +1,145 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Runs on a Gautschi node (stdlib only): episodes from a queue in parallel workers, one contained Apptainer per episode."""
2
+ import argparse
3
+ import json
4
+ import os
5
+ import queue
6
+ import shutil
7
+ import subprocess
8
+ import threading
9
+ import time
10
+
11
+ RUN = ("source /opt/ros/humble/setup.bash && source /ws/install/setup.bash"
12
+ " && python3 -m simnode.batch /batch.json /out")
13
+ CUDA_LIB = "/ws/mpc_cuda/libmpc_cuda.so" # built per stack by runner/gautschi.build_stack for gautschi-gpu
14
+ SAMPLE_S = 15
15
+ STAGGER_S = 0.25 # between worker starts: the dev job's one error was a stack starved in the 96-way start burst
16
+ EPISODE_TIMEOUT_S = 1200 # about 4x the slowest lap; a killed episode has no outcome and is retried by the host
17
+
18
+
19
+ def worker_cmd(i, work, out, ep, sif, engine, stack, gpu=None):
20
+ """Private /tmp, HOME, /dev/shm, IPC, PIDs and loopback per episode, plus a distinct ROS_DOMAIN_ID per worker;
21
+ gpu (a host CUDA device) adds the driver and the stack's CUDA MPC kernel."""
22
+ cuda = ["--nv", "--env", f"CUDA_VISIBLE_DEVICES={gpu}", "--env", f"MPC_CUDA_LIB={CUDA_LIB}"] if gpu is not None else []
23
+ if gpu is None and os.path.exists(os.path.join(stack, "mpc_cuda", "libmpc_cuda.so")):
24
+ cuda = ["--env", f"MPC_CUDA_LIB={CUDA_LIB}"] # CPU node: the library's native iLQR solve still loads; its MPPI falls back to numpy
25
+ return ["apptainer", "exec", *cuda, "--contain", "--cleanenv", "--ipc", "--pid", "--net", "--network", "none", "--workdir", work,
26
+ "--env", f"ROS_DOMAIN_ID={i + 1}", "--env", "ROS_HOME=/tmp/ros", "--env", "TMPDIR=/tmp",
27
+ "--bind", f"{engine}:/opt/sim:ro", "--bind", f"{stack}:/ws:ro", "--bind", f"{out}:/out",
28
+ "--bind", f"{ep}:/batch.json:ro", sif, "bash", "-lc", RUN]
29
+
30
+
31
+ def parse_smi(text):
32
+ """nvidia-smi 'utilization.gpu, memory.used' csv rows to per-GPU lists."""
33
+ rows = [[int(v) for v in ln.split(",")] for ln in text.strip().splitlines() if ln.strip()]
34
+ return {"gpu_util": [r[0] for r in rows], "gpu_mem_mib": [r[1] for r in rows]}
35
+
36
+
37
+ def _solver(logdir):
38
+ """pathfinder's 'MPC solver: ...' and CUDA fallback lines from the episode's ROS logs."""
39
+ lines = []
40
+ for dp, _, fns in os.walk(logdir):
41
+ for fn in fns:
42
+ try:
43
+ with open(os.path.join(dp, fn), errors="replace") as f:
44
+ lines += [ln.strip() for ln in f if "MPC solver:" in ln or "CUDA MPC unavailable" in ln]
45
+ except OSError:
46
+ pass
47
+ return "\n".join(dict.fromkeys(lines))
48
+
49
+
50
+ def main(argv=None):
51
+ p = argparse.ArgumentParser()
52
+ for a in ("episodes", "out", "sif", "engine", "stack", "work", "timeline", "done"):
53
+ p.add_argument("--" + a, required=True)
54
+ p.add_argument("--workers", type=int, required=True)
55
+ p.add_argument("--gpus", type=int, default=0)
56
+ a = p.parse_args(argv)
57
+ with open(a.episodes) as f:
58
+ q = queue.Queue()
59
+ for e in json.load(f): # the host already ordered them, longest first
60
+ q.put(e)
61
+ os.makedirs(a.out, exist_ok=True)
62
+ lock, t0 = threading.Lock(), time.time()
63
+ 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)]
64
+
65
+ def log(**ev):
66
+ with lock, open(a.timeline, "a") as f:
67
+ f.write(json.dumps(dict(ev, t=round(time.time() - t0, 3))) + "\n")
68
+
69
+ def worker(i):
70
+ time.sleep(i * STAGGER_S)
71
+ while True:
72
+ try:
73
+ e = q.get_nowait()
74
+ except queue.Empty:
75
+ return
76
+ key, work = e["key"], os.path.join(a.work, e["key"])
77
+ stage, ep = os.path.join(work, "stage"), os.path.join(work, "ep.json")
78
+ os.makedirs(stage)
79
+ with open(ep, "w") as f:
80
+ json.dump([e], f)
81
+ log(ev="start", key=key, worker=i)
82
+ gpu = devs[i % len(devs)] if devs and e["resolved"].get("recipe", {}).get("sim", {}).get("cuda") else None
83
+ limit = EPISODE_TIMEOUT_S * max(1, e["resolved"]["scenario"].get("laps", 1)) # per lap: 5-lap episodes run 5x longer
84
+ try:
85
+ rc = subprocess.run(worker_cmd(i, work, stage, ep, a.sif, a.engine, a.stack, gpu), stdout=subprocess.DEVNULL,
86
+ stderr=subprocess.DEVNULL, timeout=limit).returncode
87
+ except subprocess.TimeoutExpired:
88
+ rc = "timeout"
89
+ solver = _solver(os.path.join(work, "tmp", "ros", "log"))
90
+ if solver and os.path.isdir(os.path.join(stage, key)):
91
+ with open(os.path.join(stage, key, "solver.txt"), "w") as f:
92
+ f.write(solver + "\n")
93
+ if os.path.exists(os.path.join(stage, key, "outcome.json")):
94
+ tmp, dst = os.path.join(a.out, ".tmp-" + key), os.path.join(a.out, key)
95
+ shutil.rmtree(tmp, ignore_errors=True)
96
+ shutil.copytree(os.path.join(stage, key), tmp)
97
+ shutil.rmtree(dst, ignore_errors=True)
98
+ os.rename(tmp, dst) # the host's rsync sees a key only once its directory is complete
99
+ with lock, open(a.done, "a") as f:
100
+ f.write(key + "\n")
101
+ shutil.rmtree(work, ignore_errors=True)
102
+ log(ev="end", key=key, worker=i, rc=rc, gpu=gpu,
103
+ solver=solver.split("MPC solver: ")[-1] if "MPC solver: " in solver else solver or None)
104
+
105
+ def sampler(stop):
106
+ """Job CPU use from its cgroup, as busy cores, every SAMPLE_S."""
107
+ try:
108
+ with open("/proc/self/cgroup") as f:
109
+ path = "/sys/fs/cgroup" + f.read().strip().split(":")[-1] + "/cpu.stat"
110
+ prev = None
111
+ while not stop.wait(SAMPLE_S if prev else 0):
112
+ with open(path) as f:
113
+ use = next(int(ln.split()[1]) for ln in f if ln.startswith("usage_usec"))
114
+ now = time.time()
115
+ if prev:
116
+ log(ev="cpu", busy_cores=round((use - prev[0]) / 1e6 / (now - prev[1]), 2))
117
+ prev = (use, now)
118
+ except (OSError, StopIteration, ValueError):
119
+ pass
120
+
121
+ def gpu_sampler(stop):
122
+ """GPU utilisation and memory of the job's GPUs, every SAMPLE_S."""
123
+ while not stop.wait(SAMPLE_S):
124
+ try:
125
+ p = subprocess.Popen(["nvidia-smi", "--query-gpu=utilization.gpu,memory.used", "--format=csv,noheader,nounits"],
126
+ stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, text=True)
127
+ log(ev="gpu", **parse_smi(p.communicate(timeout=30)[0]))
128
+ except Exception: # no driver or a hung query: skip this sample
129
+ pass
130
+
131
+ cores = len(os.sched_getaffinity(0)) if hasattr(os, "sched_getaffinity") else os.cpu_count()
132
+ log(ev="launch", workers=a.workers, cores=cores)
133
+ stop = threading.Event()
134
+ threads = [threading.Thread(target=worker, args=(i,)) for i in range(a.workers)]
135
+ samplers = [sampler] + ([gpu_sampler] if a.gpus else [])
136
+ for t in threads + [threading.Thread(target=f, args=(stop,), daemon=True) for f in samplers]:
137
+ t.start()
138
+ for t in threads:
139
+ t.join()
140
+ stop.set()
141
+ log(ev="done")
142
+
143
+
144
+ if __name__ == "__main__":
145
+ main()
results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/gautschi/1/episodes.json ADDED
The diff for this file is too large to render. See raw diff
 
results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/gautschi/1/gautschi_launch.py ADDED
@@ -0,0 +1,145 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Runs on a Gautschi node (stdlib only): episodes from a queue in parallel workers, one contained Apptainer per episode."""
2
+ import argparse
3
+ import json
4
+ import os
5
+ import queue
6
+ import shutil
7
+ import subprocess
8
+ import threading
9
+ import time
10
+
11
+ RUN = ("source /opt/ros/humble/setup.bash && source /ws/install/setup.bash"
12
+ " && python3 -m simnode.batch /batch.json /out")
13
+ CUDA_LIB = "/ws/mpc_cuda/libmpc_cuda.so" # built per stack by runner/gautschi.build_stack for gautschi-gpu
14
+ SAMPLE_S = 15
15
+ STAGGER_S = 0.25 # between worker starts: the dev job's one error was a stack starved in the 96-way start burst
16
+ EPISODE_TIMEOUT_S = 1200 # about 4x the slowest lap; a killed episode has no outcome and is retried by the host
17
+
18
+
19
+ def worker_cmd(i, work, out, ep, sif, engine, stack, gpu=None):
20
+ """Private /tmp, HOME, /dev/shm, IPC, PIDs and loopback per episode, plus a distinct ROS_DOMAIN_ID per worker;
21
+ gpu (a host CUDA device) adds the driver and the stack's CUDA MPC kernel."""
22
+ cuda = ["--nv", "--env", f"CUDA_VISIBLE_DEVICES={gpu}", "--env", f"MPC_CUDA_LIB={CUDA_LIB}"] if gpu is not None else []
23
+ if gpu is None and os.path.exists(os.path.join(stack, "mpc_cuda", "libmpc_cuda.so")):
24
+ cuda = ["--env", f"MPC_CUDA_LIB={CUDA_LIB}"] # CPU node: the library's native iLQR solve still loads; its MPPI falls back to numpy
25
+ return ["apptainer", "exec", *cuda, "--contain", "--cleanenv", "--ipc", "--pid", "--net", "--network", "none", "--workdir", work,
26
+ "--env", f"ROS_DOMAIN_ID={i + 1}", "--env", "ROS_HOME=/tmp/ros", "--env", "TMPDIR=/tmp",
27
+ "--bind", f"{engine}:/opt/sim:ro", "--bind", f"{stack}:/ws:ro", "--bind", f"{out}:/out",
28
+ "--bind", f"{ep}:/batch.json:ro", sif, "bash", "-lc", RUN]
29
+
30
+
31
+ def parse_smi(text):
32
+ """nvidia-smi 'utilization.gpu, memory.used' csv rows to per-GPU lists."""
33
+ rows = [[int(v) for v in ln.split(",")] for ln in text.strip().splitlines() if ln.strip()]
34
+ return {"gpu_util": [r[0] for r in rows], "gpu_mem_mib": [r[1] for r in rows]}
35
+
36
+
37
+ def _solver(logdir):
38
+ """pathfinder's 'MPC solver: ...' and CUDA fallback lines from the episode's ROS logs."""
39
+ lines = []
40
+ for dp, _, fns in os.walk(logdir):
41
+ for fn in fns:
42
+ try:
43
+ with open(os.path.join(dp, fn), errors="replace") as f:
44
+ lines += [ln.strip() for ln in f if "MPC solver:" in ln or "CUDA MPC unavailable" in ln]
45
+ except OSError:
46
+ pass
47
+ return "\n".join(dict.fromkeys(lines))
48
+
49
+
50
+ def main(argv=None):
51
+ p = argparse.ArgumentParser()
52
+ for a in ("episodes", "out", "sif", "engine", "stack", "work", "timeline", "done"):
53
+ p.add_argument("--" + a, required=True)
54
+ p.add_argument("--workers", type=int, required=True)
55
+ p.add_argument("--gpus", type=int, default=0)
56
+ a = p.parse_args(argv)
57
+ with open(a.episodes) as f:
58
+ q = queue.Queue()
59
+ for e in json.load(f): # the host already ordered them, longest first
60
+ q.put(e)
61
+ os.makedirs(a.out, exist_ok=True)
62
+ lock, t0 = threading.Lock(), time.time()
63
+ 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)]
64
+
65
+ def log(**ev):
66
+ with lock, open(a.timeline, "a") as f:
67
+ f.write(json.dumps(dict(ev, t=round(time.time() - t0, 3))) + "\n")
68
+
69
+ def worker(i):
70
+ time.sleep(i * STAGGER_S)
71
+ while True:
72
+ try:
73
+ e = q.get_nowait()
74
+ except queue.Empty:
75
+ return
76
+ key, work = e["key"], os.path.join(a.work, e["key"])
77
+ stage, ep = os.path.join(work, "stage"), os.path.join(work, "ep.json")
78
+ os.makedirs(stage)
79
+ with open(ep, "w") as f:
80
+ json.dump([e], f)
81
+ log(ev="start", key=key, worker=i)
82
+ gpu = devs[i % len(devs)] if devs and e["resolved"].get("recipe", {}).get("sim", {}).get("cuda") else None
83
+ limit = EPISODE_TIMEOUT_S * max(1, e["resolved"]["scenario"].get("laps", 1)) # per lap: 5-lap episodes run 5x longer
84
+ try:
85
+ rc = subprocess.run(worker_cmd(i, work, stage, ep, a.sif, a.engine, a.stack, gpu), stdout=subprocess.DEVNULL,
86
+ stderr=subprocess.DEVNULL, timeout=limit).returncode
87
+ except subprocess.TimeoutExpired:
88
+ rc = "timeout"
89
+ solver = _solver(os.path.join(work, "tmp", "ros", "log"))
90
+ if solver and os.path.isdir(os.path.join(stage, key)):
91
+ with open(os.path.join(stage, key, "solver.txt"), "w") as f:
92
+ f.write(solver + "\n")
93
+ if os.path.exists(os.path.join(stage, key, "outcome.json")):
94
+ tmp, dst = os.path.join(a.out, ".tmp-" + key), os.path.join(a.out, key)
95
+ shutil.rmtree(tmp, ignore_errors=True)
96
+ shutil.copytree(os.path.join(stage, key), tmp)
97
+ shutil.rmtree(dst, ignore_errors=True)
98
+ os.rename(tmp, dst) # the host's rsync sees a key only once its directory is complete
99
+ with lock, open(a.done, "a") as f:
100
+ f.write(key + "\n")
101
+ shutil.rmtree(work, ignore_errors=True)
102
+ log(ev="end", key=key, worker=i, rc=rc, gpu=gpu,
103
+ solver=solver.split("MPC solver: ")[-1] if "MPC solver: " in solver else solver or None)
104
+
105
+ def sampler(stop):
106
+ """Job CPU use from its cgroup, as busy cores, every SAMPLE_S."""
107
+ try:
108
+ with open("/proc/self/cgroup") as f:
109
+ path = "/sys/fs/cgroup" + f.read().strip().split(":")[-1] + "/cpu.stat"
110
+ prev = None
111
+ while not stop.wait(SAMPLE_S if prev else 0):
112
+ with open(path) as f:
113
+ use = next(int(ln.split()[1]) for ln in f if ln.startswith("usage_usec"))
114
+ now = time.time()
115
+ if prev:
116
+ log(ev="cpu", busy_cores=round((use - prev[0]) / 1e6 / (now - prev[1]), 2))
117
+ prev = (use, now)
118
+ except (OSError, StopIteration, ValueError):
119
+ pass
120
+
121
+ def gpu_sampler(stop):
122
+ """GPU utilisation and memory of the job's GPUs, every SAMPLE_S."""
123
+ while not stop.wait(SAMPLE_S):
124
+ try:
125
+ p = subprocess.Popen(["nvidia-smi", "--query-gpu=utilization.gpu,memory.used", "--format=csv,noheader,nounits"],
126
+ stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, text=True)
127
+ log(ev="gpu", **parse_smi(p.communicate(timeout=30)[0]))
128
+ except Exception: # no driver or a hung query: skip this sample
129
+ pass
130
+
131
+ cores = len(os.sched_getaffinity(0)) if hasattr(os, "sched_getaffinity") else os.cpu_count()
132
+ log(ev="launch", workers=a.workers, cores=cores)
133
+ stop = threading.Event()
134
+ threads = [threading.Thread(target=worker, args=(i,)) for i in range(a.workers)]
135
+ samplers = [sampler] + ([gpu_sampler] if a.gpus else [])
136
+ for t in threads + [threading.Thread(target=f, args=(stop,), daemon=True) for f in samplers]:
137
+ t.start()
138
+ for t in threads:
139
+ t.join()
140
+ stop.set()
141
+ log(ev="done")
142
+
143
+
144
+ if __name__ == "__main__":
145
+ main()
results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/job.json ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "id": "20261105-024128-A26_MPPI___Adaptive_Gain",
3
+ "tier": "dev",
4
+ "tier_version": 2,
5
+ "recipe": {
6
+ "name": "A26 MPPI + Adaptive Gain",
7
+ "source": {
8
+ "repo": "AutonomousKart",
9
+ "ref": "worktree:AutonomousKart-sim"
10
+ },
11
+ "params": {
12
+ "pathfinder_node": {
13
+ "planner": "mpc",
14
+ "mpc.num_samples": 32768,
15
+ "mpc.adaptive_gain.enabled": true
16
+ }
17
+ },
18
+ "sim": {
19
+ "cuda": true,
20
+ "active_imu": "back",
21
+ "use_camera": false,
22
+ "controller_type": "mppi",
23
+ "baseline": true
24
+ },
25
+ "notes": "A26 MPPI sampling rollout stack with adaptive steering gain (SteerGainRLS, 32768 samples, back IMU imu_2, no camera)"
26
+ },
27
+ "user": "shay",
28
+ "notes": "",
29
+ "where": "ideas",
30
+ "status": "done",
31
+ "submitted_at": "2026-11-05T07:41:28.254+00:00",
32
+ "started_at": "2026-11-05T07:41:28.256+00:00",
33
+ "finished_at": "2026-11-05T08:37:57.322+00:00",
34
+ "total": 186,
35
+ "done": 186,
36
+ "errors": 147,
37
+ "build_log": null,
38
+ "workers": 10,
39
+ "slurm_id": "661",
40
+ "slurm_ids": [
41
+ "4624",
42
+ "661"
43
+ ],
44
+ "engine_version": "bbd85e0dce441d49ae06ba82dddb973a04f86a9fdd0705c67c5e131c98efe8b5"
45
+ }
results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/summary.json ADDED
@@ -0,0 +1,292 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cte_p50": {
3
+ "value": 0.20890318738745103,
4
+ "worst_kart": null,
5
+ "per_kart": {
6
+ "kart1": 0.20890318738745103
7
+ }
8
+ },
9
+ "cte_p90": {
10
+ "value": 0.5996545817232048,
11
+ "worst_kart": null,
12
+ "per_kart": {
13
+ "kart1": 0.5996545817232048
14
+ }
15
+ },
16
+ "cte_p99": {
17
+ "value": 1.2580454697570194,
18
+ "worst_kart": null,
19
+ "per_kart": {
20
+ "kart1": 1.2580454697570194
21
+ }
22
+ },
23
+ "recovery_rate": {
24
+ "value": null,
25
+ "worst_kart": null,
26
+ "per_kart": {
27
+ "kart1": null
28
+ }
29
+ },
30
+ "recovery_s_median": {
31
+ "value": null,
32
+ "worst_kart": null,
33
+ "per_kart": {
34
+ "kart1": null
35
+ }
36
+ },
37
+ "safety": {
38
+ "value": 0.8854618600615279,
39
+ "worst_kart": null,
40
+ "per_kart": {
41
+ "kart1": 0.8854618600615279
42
+ }
43
+ },
44
+ "unsafe_lat": {
45
+ "value": 0.0016755840743349763,
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+ "worst_kart": null,
47
+ "per_kart": {
48
+ "kart1": 0.0016755840743349763
49
+ }
50
+ },
51
+ "unsafe_rate": {
52
+ "value": 0.060701841238409666,
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+ "worst_kart": null,
54
+ "per_kart": {
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+ "kart1": 0.060701841238409666
56
+ }
57
+ },
58
+ "unsafe_travel": {
59
+ "value": 0.0,
60
+ "worst_kart": null,
61
+ "per_kart": {
62
+ "kart1": 0.0
63
+ }
64
+ },
65
+ "unsafe_spin": {
66
+ "value": 0.00011696447272143521,
67
+ "worst_kart": null,
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+ "per_kart": {
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+ "kart1": 0.00011696447272143521
70
+ }
71
+ },
72
+ "unsafe_off": {
73
+ "value": 0.05693177707115536,
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+ "worst_kart": null,
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+ "per_kart": {
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+ "kart1": 0.05693177707115536
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+ }
78
+ },
79
+ "osc_10": {
80
+ "value": 27.13643323460292,
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+ "worst_kart": null,
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+ "per_kart": {
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+ "kart1": 27.13643323460292
84
+ }
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+ },
86
+ "osc_20": {
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+ "value": 12.520783736971085,
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+ "worst_kart": null,
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+ "per_kart": {
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+ "kart1": 12.520783736971085
91
+ }
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+ },
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+ "osc_30": {
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+ "value": 8.028679332317289,
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+ "worst_kart": null,
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+ "per_kart": {
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+ "kart1": 8.028679332317289
98
+ }
99
+ },
100
+ "laps_pct": {
101
+ "value": 53.84615384615385,
102
+ "worst_kart": null,
103
+ "per_kart": {
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+ "kart1": 53.84615384615385
105
+ }
106
+ },
107
+ "failures": {
108
+ "value": 18,
109
+ "worst_kart": null,
110
+ "per_kart": {
111
+ "kart1": 18
112
+ }
113
+ },
114
+ "loc_rms": {
115
+ "value": 0.35,
116
+ "worst_kart": null,
117
+ "per_kart": {
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+ "kart1": 0.35
119
+ }
120
+ },
121
+ "loc_p50": {
122
+ "value": 0.334,
123
+ "worst_kart": null,
124
+ "per_kart": {
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+ "kart1": 0.334
126
+ }
127
+ },
128
+ "loc_p95": {
129
+ "value": 0.372,
130
+ "worst_kart": null,
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+ "per_kart": {
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+ "kart1": 0.372
133
+ }
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+ },
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+ "loc_p99": {
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+ "value": 0.39,
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+ "worst_kart": null,
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+ "per_kart": {
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+ "kart1": 0.39
140
+ }
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+ },
142
+ "best_lap_s": {
143
+ "value": 72.383,
144
+ "worst_kart": null,
145
+ "per_kart": {
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+ "kart1": 72.383
147
+ }
148
+ },
149
+ "lap_time_90": {
150
+ "value": 98.57,
151
+ "worst_kart": null,
152
+ "per_kart": {
153
+ "kart1": 98.57
154
+ }
155
+ },
156
+ "lap_time_99": {
157
+ "value": 105.089,
158
+ "worst_kart": null,
159
+ "per_kart": {
160
+ "kart1": 105.089
161
+ }
162
+ },
163
+ "lap_time_999": {
164
+ "value": 105.741,
165
+ "worst_kart": null,
166
+ "per_kart": {
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+ "kart1": 105.741
168
+ }
169
+ },
170
+ "learned_to_slide_rate": {
171
+ "value": 0.0,
172
+ "worst_kart": null,
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+ "per_kart": {
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+ "kart1": 0.0
175
+ }
176
+ },
177
+ "laps_to_slide": {
178
+ "value": null,
179
+ "worst_kart": null,
180
+ "per_kart": {
181
+ "kart1": null
182
+ }
183
+ },
184
+ "slide_pct_p95": {
185
+ "value": 60.63,
186
+ "worst_kart": null,
187
+ "per_kart": {
188
+ "kart1": 60.63
189
+ }
190
+ },
191
+ "lap_improvement_s": {
192
+ "value": 0.0,
193
+ "worst_kart": null,
194
+ "per_kart": {
195
+ "kart1": 0.0
196
+ }
197
+ },
198
+ "overtake_rate": {
199
+ "value": null,
200
+ "worst_kart": null,
201
+ "per_kart": {
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+ "kart1": null
203
+ }
204
+ },
205
+ "overtake_s_median": {
206
+ "value": null,
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+ "worst_kart": null,
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+ "per_kart": {
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+ "kart1": null
210
+ }
211
+ },
212
+ "min_clearance_m": {
213
+ "value": null,
214
+ "worst_kart": null,
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+ "per_kart": {
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+ "kart1": null
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+ }
218
+ },
219
+ "collisions": {
220
+ "value": 0,
221
+ "worst_kart": null,
222
+ "per_kart": {
223
+ "kart1": 0
224
+ }
225
+ },
226
+ "collision_rate": {
227
+ "value": null,
228
+ "worst_kart": null,
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+ "per_kart": {
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+ "kart1": null
231
+ }
232
+ },
233
+ "score_control": {
234
+ "value": 51.9,
235
+ "worst_kart": null,
236
+ "per_kart": {
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+ "kart1": 51.9
238
+ }
239
+ },
240
+ "score_localization": {
241
+ "value": 0.0,
242
+ "worst_kart": null,
243
+ "per_kart": {
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+ "kart1": 0.0
245
+ }
246
+ },
247
+ "score_overtake": {
248
+ "value": null,
249
+ "worst_kart": null,
250
+ "per_kart": {
251
+ "kart1": null
252
+ }
253
+ },
254
+ "score_slide": {
255
+ "value": null,
256
+ "worst_kart": null,
257
+ "per_kart": {
258
+ "kart1": null
259
+ }
260
+ },
261
+ "score_endurance": {
262
+ "value": null,
263
+ "worst_kart": null,
264
+ "per_kart": {
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+ "kart1": null
266
+ }
267
+ },
268
+ "score_clips": {
269
+ "value": null,
270
+ "worst_kart": null,
271
+ "per_kart": {
272
+ "kart1": null
273
+ }
274
+ },
275
+ "composite_score": {
276
+ "value": 43.2,
277
+ "worst_kart": null,
278
+ "per_kart": {
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+ "kart1": 43.2
280
+ }
281
+ },
282
+ "n": {
283
+ "value": 186,
284
+ "worst_kart": null,
285
+ "per_kart": {}
286
+ },
287
+ "errors": {
288
+ "value": 147,
289
+ "worst_kart": null,
290
+ "per_kart": {}
291
+ }
292
+ }
results/leaderboard/2__bbd85e0dce44.json CHANGED
@@ -186,5 +186,99 @@
186
  "n": 186,
187
  "errors": 20
188
  }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
189
  }
190
  ]
 
186
  "n": 186,
187
  "errors": 20
188
  }
189
+ },
190
+ {
191
+ "job_id": "20261105-024128-A26_MPPI___Adaptive_Gain",
192
+ "recipe": "A26 MPPI + Adaptive Gain",
193
+ "notes": "",
194
+ "user": "shay",
195
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  }
284
  ]