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runs/embodied-olympics-match-badminton-match-i01-privileged--wzskquk/replay.mp4

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runs/embodied-olympics-match-badminton-match-i02-privileged--pkasxua/replay.mp4

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runs/embodied-olympics-match-soccer-match-i00-privileged--neohjec/replay.mp4

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runs/embodied-olympics-match-soccer-match-i00-privileged--ufuxjhv.json

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+{"slug":"embodied-olympics-match-soccer-match-i00-privileged--ufuxjhv","task":"embodied-olympics-match-soccer-match-i00-privileged","bench":"embodied-olympics-match","mode":"privileged","run":"antigravity_cli-gemini_4_argon-high","agent":"antigravity_sub:AntigravitySubscription","model":"gemini-4-argon","effort":"high","state":"success","success":1,"reward":{"success":1,"valid":1,"deterministic":1,"infra_error":0,"policy_error":0,"calls":7700,"refused":0,"level":2,"seed":7,"slowdown":5,"replays":1,"isolation_checked":1,"processes_left":0,"points_for":10,"points_against":0,"steps":11775,"late_max_s":0.011134461732581258,"resource_enforced":0,"limits_violation_observed":0,"resource_monitor_missing":0,"peak_gpu_memory_gb":0,"peak_memory_gb":1.328,"peak_processes":10,"peak_disk_gb":0.247,"resource_emergency_kills":0,"resource_oom_kills":0,"resource_host_oom_kills":0,"resource_service_oom_kills":0},"wall_s":2747,"tokens":{"in":5051410,"cached":4639016,"out":50537,"usd":2.08},"task_text":"","step_times":"recorded","sim_total":{"submit":0,"reset":0},"replay":"embodied-olympics-match-soccer-match-i00-privileged--ufuxjhv/replay.mp4","replay_last":"embodied-olympics-match-soccer-match-i00-privileged--ufuxjhv/replay_last.jpg","steps":[{"id":2,"t":0.0,"say":"","calls":[{"kind":"shell","label":"reads README.md","code":"/opt/fullsim/README.md"},{"kind":"shell","label":"Listing directories and checking limits","code":"ls -la /app /opt/fullsim /run/rcb-limits/usage.json && cat /run/rcb-limits/usage.json"}],"out":{"text":"File Path: `file:///opt/fullsim/README.md`\nTotal Lines: 89\nTotal Bytes: 6088\nShowing lines 1 to 89\nThe following code has been modified to include a line number before every line, in the format: <line_number>: <original_line>. Please note that any changes targeting the original code should remove the line number, colon, and leading space.\n1: # Full-simulator program task: soccer, level 2, seed 7\n2: \n3: The development source at `/opt/fullsim` is complete and CPU-only: the frozen simulator of `roboolympics-match-soccer-match-i00-standard-realtime` (this task's\n4: camera-only twin), its referee and its scripted opponent. No GPU, renderer or simulator service is needed. Run the\n5: commands inside the task's agent container from `/app`.\n6: \n7: ## First command: inspect, step and reset the actual simulator\n8: \n9: ```bash\n10: python -c 'from tools.fullsim.session import Session; s=Session(\"soccer\", level=2, seed=7); print(s.state()[\"scores\"]); s.step(10); print(s.reset()[\"state\"][\"step\"])'\n11: ```\n12: \n13: `s.world` is the entire mutable Match object: the simulator, the scripted opponent, the referee and the planners.\n14: `s.spec()` gives the original action interface and the rules as the twin's `game spec` states them. `act(command)`\n15: handles an action as the live service does (parse, plan, lower, apply); `step(n)` advances `n` control steps and\n16: stops at a review (after a point, a goal, ...) or the end of the match; `ready()` leaves the review; `reset()` rebuilds the whole \n… (5,142 characters left out) …\nlay.mp4`\n88: as the twin's service draws its video, for spectators only (nothing reads it back; a failed render leaves no video).\n89: \nThe above content shows the entire, complete file contents of the requested file.\nThe command exited with code 0.\nOutput:\n-rw-r--r-- 1 root root  478 Oct 19 16:29 /run/rcb-limits/usage.json\r\n\r\n/app:\r\ntotal 12\r\ndrwxr-xr-x 1 root root 4096 Oct 19 16:27 .\r\ndrwxr-xr-x 1 root root 4096 Oct 19 16:28 ..\r\ndrwxr-xr-x 2 root root 4096 Oct 19 16:27 output\r\n\r\n/opt/fullsim:\r\ntotal 44\r\ndrwxr-xr-x 1 root root 4096 Oct 19 16:27 .\r\ndrwxr-xr-x 1 root root 4096 Oct 19 16:27 ..\r\n-rw-r--r-- 1 root root 6088 Apr 28  2003 README.md\r\ndrwxr-xr-x 1 root root 4096 Oct 19 16:27 bodies\r\ndrwxr-xr-x 1 root root 4096 Oct 19 16:27 core\r\ndrwxr-xr-x 2 root root 4096 Oct 19 16:27 metering\r\ndrwxr-xr-x 1 root root 4096 Oct 19 16:27 renderers\r\ndrwxr-xr-x 2 root root 4096 Oct 19 16:27 spectator\r\ndrwxr-xr-x 1 root root 4096 Oct 18 23:55 sports\r\ndrwxr-xr-x 1 root root 4096 Oct 18 23:55 tools\r\n{\r\n \"t\": 1792427340.88,\r\n \"measured\": true,\r\n \"gpu_memory_gb\": 0.0,\r\n \"memory_gb\": 0.092,\r\n \"processes\": 5,\r\n \"disk_gb\": 0.0,\r\n \"enforced\": 0,\r\n \"limits\": {\r\n  \"max_gpu_memory_gb\": 0.0,\r\n  \"max_memory_gb\": 13.0,\r\n  \"max_processes\": 512,\r\n  \"max_disk_gb\": 20.0,\r\n  \"sustain_sec\": 60.0,\r\n  \"window_sec\": 120.0\r\n },\r\n \"violations\": [],\r\n \"emergency_kills\": 0,\r\n \"last_emergency_kill\": null,\r\n \"elapsed_s\": 25.0,\r\n \"time_limit_s\": 7200,\r\n \"time_left_s\": 7175.0,\r\n \"deadline_utc\": \"2026-10-19T18:28:35Z\"\r\n}","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":3,"t":8.0,"say":"","calls":[{"kind":"shell","label":"Finding soccer and tool files","code":"find /opt/fullsim/sports/soccer /opt/fullsim/tools /app/output -maxdepth 3"}],"out":{"text":"The command exited with code 0.\nOutput:\n/opt/fullsim/sports/soccer\r\n/opt/fullsim/sports/soccer/soccer_scene.py\r\n/opt/fullsim/sports/soccer/soccer.py\r\n/opt/fullsim/sports/soccer/swing_kick.py\r\n/opt/fullsim/sports/soccer/models\r\n/opt/fullsim/sports/soccer/models/NOTICE.md\r\n/opt/fullsim/sports/soccer/models/config\r\n/opt/fullsim/sports/soccer/models/config/lower_t1.yaml\r\n/opt/fullsim/sports/soccer/models/robot\r\n/opt/fullsim/sports/soccer/models/robot/lower_t1_control.pt\r\n/opt/fullsim/sports/soccer/plugin.py\r\n/opt/fullsim/sports/soccer/sport.toml\r\n/opt/fullsim/sports/soccer/step_kick.py\r\n/opt/fullsim/sports/soccer/overhead.py\r\n/opt/fullsim/sports/soccer/grade.py\r\n/opt/fullsim/tools\r\n/opt/fullsim/tools/__init__.py\r\n/opt/fullsim/tools/fullsim\r\n/opt/fullsim/tools/fullsim/sports\r\n/opt/fullsim/tools/fullsim/sports/__init__.py\r\n/opt/fullsim/tools/fullsim/sports/soccer.py\r\n/opt/fullsim/tools/fullsim/program.py\r\n/opt/fullsim/tools/fullsim/__init__.py\r\n/opt/fullsim/tools/fullsim/worker.py\r\n/opt/fullsim/tools/fullsim/session.py\r\n/opt/fullsim/tools/fullsim/formal.py\r\n/opt/fullsim/tools/fullsim/adapter.py\r\n/app/output","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":4,"t":21.0,"say":"","calls":[{"kind":"shell","label":"reads sport.toml","code":"/opt/fullsim/sports/soccer/sport.toml"},{"kind":"shell","label":"reads plugin.py","code":"/opt/fullsim/sports/soccer/plugin.py"},{"kind":"shell","label":"reads soccer.py","code":"/opt/fullsim/sports/soccer/soccer.py"},{"kind":"shell","label":"reads soccer.py","code":"/opt/fullsim/tools/fullsim/sports/soccer.py"}],"out":{"text":"File Path: `file:///opt/fullsim/sports/soccer/sport.toml`\nTotal Lines: 363\nTotal Bytes: 34003\nShowing lines 1 to 363\nThe following code has been modified to include a line number before every line, in the format: <line_number>: <original_line>. Please note that any changes targeting the original code should remove the line number, colon, and leading space.\n1: # soccer (docs/framework_zh.md §4.2, §10 stage 4): robot soccer 2 v 2, migrated from the legacy tree\n2: # (scripts/olympiad/soccer/, scripts/olympiad/shared/soccer/sim/), the simulator unchanged (soccer.py, soccer_scene.py,\n3: # models/). Three formats on one world: `match` (one seat against the scripted team), `duel` (format A: one seat per\n4: # team) and `team` (format B: one seat per robot, the team channel between teammates). Revision soccer-2v2-4\n5: # (2026-10-09): the robots are held to the real Booster T1's limits (bodies/booster_t1: leg torques, joint speeds as a\n6: # torque-speed envelope, walking-command rate; bodies/servo.py) and the platform kick is a real swing-leg kick (swing_kick.py); the\n7: # legacy recordings of soccer-2v2-3 no longer replay (golden/convert.py retires them).\n8: \n9: [sport]\n10: name = \"soccer\"\n11: title = \"Robot soccer 2 v 2\"\n12: summary = \"robot soccer 2 v 2 under the RoboCup Humanoid Soccer League rules: four simulated Booster T1 humanoids (lower bodies on the released walking policy, held to the real robot's joint limits) and one ball on a boarded 10 x 7 m pitch with two goals, two hal\n… (125,367 characters left out) …\n\"])},\n89:                \"free_kick\": None if not fk else {\"team\": fk[\"team\"], \"kind\": fk[\"kind\"], \"spot\": to(fk[\"spot\"]),\n90:                                                  \"in_play\": bool(fk[\"in_play\"])},\n91:                \"shootout\": None if not cur else {\"n\": int(cur[\"n\"]), \"team\": cur[\"team\"], \"striker\": cur[\"striker\"],\n92:                                                  \"keeper\": cur[\"keeper\"]}}\n93:     return {\"k\": int(w.k),\n94:             \"ball\": {\"pos\": to(bp[:2]) + [_f(bp[2])], \"vel\": to(bv[:2]) + [_f(bv[2])],\n95:                      \"spin\": [_f(v) for v in bv[3:6]]},\n96:             \"robots\": robots, \"referee\": referee,\n97:             \"qpos\": [float(x) for x in d.qpos], \"qvel\": [float(x) for x in d.qvel]}\n98: \n99: \n100: \n101: def unavailable():\n102:     \"\"\"The soccer simulator needs its image (efe:olympiad-soccer-<version>): PyTorch for the T1 walking policy\n103:     and the T1 model at $RCB_T1_ASSETS. Hosts without them (the repository's CI) skip it in the generic tests.\"\"\"\n104:     import importlib.util\n105:     import os\n106:     if importlib.util.find_spec(\"torch\") is None:\n107:         return \"PyTorch missing (the T1 walking policy; the soccer image has it)\"\n108:     if not os.path.isdir(os.environ.get(\"RCB_T1_ASSETS\", \"/opt/sai_t1\")):\n109:         return \"the Booster T1 model ($RCB_T1_ASSETS) missing (the soccer image has it)\"\n110:     return None\n111: \nThe above content shows the entire, complete file contents of the requested file.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":5,"t":29.0,"say":"","calls":[{"kind":"shell","label":"reads soccer.py","code":"/opt/fullsim/sports/soccer/soccer.py"},{"kind":"shell","label":"reads soccer.py","code":"/opt/fullsim/sports/soccer/soccer.py"},{"kind":"shell","label":"reads session.py","code":"/opt/fullsim/tools/fullsim/session.py"},{"kind":"shell","label":"reads formal.py","code":"/opt/fullsim/tools/fullsim/formal.py"}],"out":{"text":"File Path: `file:///opt/fullsim/sports/soccer/soccer.py`\nTotal Lines: 2404\nTotal Bytes: 136934\nShowing lines 750 to 1549\nThe following code has been modified to include a line number before every line, in the format: <line_number>: <original_line>. Please note that any changes targeting the original code should remove the line number, colon, and leading space.\n750:             self.prog[name] = dict(type=\"stand\")\n751: \n752:     def _step_over(self, name):\n753:         \"\"\"the in-walk kick is over: the policy takes the kicking leg again in its late swing (it puts the foot down)\n754:         and stops; or the ball never came in reach (called off)\"\"\"\n755:         p = self.prog[name]\n756:         kk = p[\"kicker\"]\n757:         if kk.cancelled:\n758:             self.stats[name][\"kicks_cancelled\"] += 1\n759:             self.event(\"kick_cancelled\", robot=name, why=kk.why)\n760:             self.prog[name] = dict(type=\"stand\")\n761:             return\n762:         self.pol.gait[self.rob[name].idx] = kk.gait_release()\n763:         p[\"recover_k\"] = self.k + int(round(STEP[\"recover_s\"] / CONTROL_DT))\n764: \n765:     def _abort_kick(self, name):\n766:         \"\"\"A robot that falls during a kick: the kick ends (the robot lies still until it is stood up).\"\"\"\n767:         p = self.prog[name]\n768:         if p[\"type\"] == \"kick\":\n769:             if p[\"style\"] == \"swing\":\n770:                 self.stats[name][\"kick_falls\"] += 1\n771:             self.prog[name] = dict(type=\"stand\")\n772: \n773:     # \n… (123,411 characters left out) …\nwrite_text(str(result[\"success\"]) + \"\\n\")\n361:     (out / \"reward.json\").write_text(json.dumps(result, indent=2))\n362:     (out / \"diagnostics.json\").write_text(json.dumps(diagnostics, indent=2))\n363:     return result\n364: \n365: \n366: def main():\n367:     p = argparse.ArgumentParser(__doc__)\n368:     p.add_argument(\"--sport\", required=True, help=\"the adapter: tools/fullsim/sports/<sport>.py\")\n369:     p.add_argument(\"--policy\", type=Path, required=True)\n370:     p.add_argument(\"--out\", type=Path, required=True)\n371:     p.add_argument(\"--level\", type=int, required=True)\n372:     p.add_argument(\"--seed\", type=int, required=True)\n373:     p.add_argument(\"--dev-root\", type=Path)\n374:     p.add_argument(\"--wall-limit\", type=float, help=\"default: the adapter's MATCH_WALL_S\")\n375:     p.add_argument(\"--slowdown\", type=float, help=\"default: the adapter's SLOWDOWN (the twin's)\")\n376:     p.add_argument(\"--unsafe-local\", action=\"store_true\")\n377:     args = p.parse_args()\n378:     result = evaluate(\n379:         args.policy,\n380:         args.out,\n381:         sport=args.sport,\n382:         level=args.level,\n383:         seed=args.seed,\n384:         dev_root=args.dev_root,\n385:         wall_limit=args.wall_limit,\n386:         slowdown=args.slowdown,\n387:         unsafe_local=args.unsafe_local,\n388:     )\n389:     print(json.dumps(result))\n390: \n391: \n392: if __name__ == \"__main__\":\n393:     main()\n394: \nThe above content shows the entire, complete file contents of the requested file.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":6,"t":38.0,"say":"","calls":[{"kind":"shell","label":"reads soccer.py","code":"/opt/fullsim/sports/soccer/soccer.py"},{"kind":"shell","label":"reads step_kick.py","code":"/opt/fullsim/sports/soccer/step_kick.py"},{"kind":"shell","label":"reads program.py","code":"/opt/fullsim/tools/fullsim/program.py"}],"out":{"text":"File Path: `file:///opt/fullsim/sports/soccer/soccer.py`\nTotal Lines: 2404\nTotal Bytes: 136934\nShowing lines 2340 to 2404\nThe following code has been modified to include a line number before every line, in the format: <line_number>: <original_line>. Please note that any changes targeting the original code should remove the line number, colon, and leading space.\n2340:             there = float(np.hypot(*(S - p))) < 0.06 and abs(_wrap(face - yaw)) < 0.06\n2341:             if there and t_arr is not None and t_arr > PASS[\"arm_s\"]:\n2342:                 out[recv] = (\"cmd\", dict(type=\"kick\", power=1.0, aim=0.0, at=[round(t_arr, 3), float(X[0]), float(X[1])]))\n2343:                 pl[\"armed\"] = True\n2344:                 return out\n2345:             if pl[\"struck_k\"] is None or (t_arr is not None and t_arr > PASS[\"arm_s\"]):\n2346:                 out[recv] = (\"receive\", dict(spot=S, face=face, vmax=0.15 if float(np.hypot(*(S - p))) < 0.4 else None))\n2347:                 return out\n2348:             pl[\"mode\"] = \"block\"                         # too late to arm: stop the ball instead\n2349:         # block: the trunk on the ball's path, facing the ball; after the pass, the earliest reachable point\n2350:         face = math.atan2(-u[1], -u[0])\n2351:         if pl[\"struck_k\"] is None:\n2352:             spot = X\n2353:         else:\n2354:             spot = None\n2355:             for i in range(1, 60):\n2356:                 t = 0.1 * i\n2357:                 dist = v * t - 0.5 * ROLL_DECE\n… (23,876 characters left out) …\n                 except OSError:\n173:                     pass\n174:         time.sleep(0.05)\n175:     return not uid_processes(uid)\n176: \n177: \n178: def sanitize(source, destination):\n179:     \"\"\"Copy bounded regular files only. Never follow an agent-controlled link.\"\"\"\n180:     if not source.is_dir() or source.is_symlink():\n181:         raise ValueError(\"submit a real policy/ directory containing program.py\")\n182:     total = count = 0\n183:     destination.mkdir(mode=0o755)\n184:     for p in sorted(source.rglob(\"*\")):\n185:         mode = p.lstat().st_mode\n186:         target = destination / p.relative_to(source)\n187:         if stat.S_ISDIR(mode):\n188:             target.mkdir(mode=0o755, exist_ok=True)\n189:             continue\n190:         if not stat.S_ISREG(mode) or p.is_symlink():\n191:             raise ValueError(\"policy contains a symlink, device, FIFO or socket\")\n192:         count += 1\n193:         total += p.stat().st_size\n194:         if count > 512 or total > 32 * 1024**2:\n195:             raise ValueError(\"policy exceeds 512 files or 32 MiB\")\n196:         # Files are copied, not imported/executed by the trusted evaluator.\n197:         with p.open(\"rb\") as src, target.open(\"xb\") as dst:\n198:             shutil.copyfileobj(src, dst)\n199:         target.chmod(0o644)\n200:     if not (destination / \"program.py\").is_file():\n201:         raise ValueError(\"missing policy/program.py\")\n202: \nThe above content shows the entire, complete file contents of the requested file.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":7,"t":56.0,"say":"","calls":[{"kind":"shell","label":"Listing all files in /opt/fullsim","code":"find /opt/fullsim -maxdepth 4"}],"out":{"text":"The command exited with code 0.\nOutput:\n/opt/fullsim\r\n/opt/fullsim/sports\r\n/opt/fullsim/sports/soccer\r\n/opt/fullsim/sports/soccer/soccer_scene.py\r\n/opt/fullsim/sports/soccer/soccer.py\r\n/opt/fullsim/sports/soccer/swing_kick.py\r\n/opt/fullsim/sports/soccer/models\r\n/opt/fullsim/sports/soccer/models/NOTICE.md\r\n/opt/fullsim/sports/soccer/models/config\r\n/opt/fullsim/sports/soccer/models/robot\r\n/opt/fullsim/sports/soccer/plugin.py\r\n/opt/fullsim/sports/soccer/sport.toml\r\n/opt/fullsim/sports/soccer/step_kick.py\r\n/opt/fullsim/sports/soccer/overhead.py\r\n/opt/fullsim/sports/soccer/grade.py\r\n/opt/fullsim/bodies\r\n/opt/fullsim/bodies/booster_t1\r\n/opt/fullsim/bodies/booster_t1/body.toml\r\n/opt/fullsim/bodies/servo.py\r\n/opt/fullsim/bodies/sources.py\r\n/opt/fullsim/bodies/limits.py\r\n/opt/fullsim/core\r\n/opt/fullsim/core/metering\r\n/opt/fullsim/core/metering/__init__.py\r\n/opt/fullsim/core/metering/api_errors.py\r\n/opt/fullsim/core/metering/think_clock.py\r\n/opt/fullsim/core/README.md\r\n/opt/fullsim/core/tests\r\n/opt/fullsim/core/tests/fixture_turn\r\n/opt/fullsim/core/tests/fixture_turn/sport.toml\r\n/opt/fullsim/core/tests/fixture_turn/plugin.py\r\n/opt/fullsim/core/tests/fixture_images\r\n/opt/fullsim/core/tests/fixture_images/sport.toml\r\n/opt/fullsim/core/tests/fixture_images/plugin.py\r\n/opt/fullsim/core/tests/fixture\r\n/opt/fullsim/core/tests/fixture/sport.toml\r\n/opt/fullsim/core/tests/fixture/plugin.py\r\n/opt/fullsim/core/tests/test_core.py\r\n/opt/fullsim/core/tests/test_diagnose.py\r\n/opt/fullsim/core/rng.py\r\n… (399 characters left out) …\nn.py\r\n/opt/fullsim/core/spectator.py\r\n/opt/fullsim/core/workers.py\r\n/opt/fullsim/core/launch.py\r\n/opt/fullsim/core/testkit.py\r\n/opt/fullsim/core/score.py\r\n/opt/fullsim/core/serve.py\r\n/opt/fullsim/core/realtime.py\r\n/opt/fullsim/core/client.py\r\n/opt/fullsim/core/local.py\r\n/opt/fullsim/core/episode.py\r\n/opt/fullsim/core/grading.py\r\n/opt/fullsim/README.md\r\n/opt/fullsim/tools\r\n/opt/fullsim/tools/__init__.py\r\n/opt/fullsim/tools/fullsim\r\n/opt/fullsim/tools/fullsim/sports\r\n/opt/fullsim/tools/fullsim/sports/__init__.py\r\n/opt/fullsim/tools/fullsim/sports/soccer.py\r\n/opt/fullsim/tools/fullsim/program.py\r\n/opt/fullsim/tools/fullsim/__init__.py\r\n/opt/fullsim/tools/fullsim/worker.py\r\n/opt/fullsim/tools/fullsim/session.py\r\n/opt/fullsim/tools/fullsim/formal.py\r\n/opt/fullsim/tools/fullsim/adapter.py\r\n/opt/fullsim/spectator\r\n/opt/fullsim/spectator/broadcast_log.py\r\n/opt/fullsim/renderers\r\n/opt/fullsim/renderers/__init__.py\r\n/opt/fullsim/renderers/export.py\r\n/opt/fullsim/renderers/pathtrace.py\r\n/opt/fullsim/renderers/scene_check.py\r\n/opt/fullsim/renderers/label.py\r\n/opt/fullsim/renderers/base.py\r\n/opt/fullsim/renderers/budget.py\r\n/opt/fullsim/renderers/mujoco_showcase\r\n/opt/fullsim/renderers/mujoco_showcase/__init__.py\r\n/opt/fullsim/renderers/mujoco\r\n/opt/fullsim/renderers/mujoco/__init__.py\r\n/opt/fullsim/renderers/isaac\r\n/opt/fullsim/renderers/isaac/__init__.py\r\n/opt/fullsim/renderers/cycles\r\n/opt/fullsim/renderers/cycles/__init__.py\r\n/opt/fullsim/metering\r\n/opt/fullsim/metering/think_proxy.py","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":8,"t":78.0,"say":"","calls":[{"kind":"shell","label":"reads worker.py","code":"/opt/fullsim/tools/fullsim/worker.py"},{"kind":"shell","label":"reads realtime.py","code":"/opt/fullsim/core/realtime.py"}],"out":{"text":"File Path: `file:///opt/fullsim/tools/fullsim/worker.py`\nTotal Lines: 45\nTotal Bytes: 1659\nShowing lines 1 to 45\nThe following code has been modified to include a line number before every line, in the format: <line_number>: <original_line>. Please note that any changes targeting the original code should remove the line number, colon, and leading space.\n1: \"\"\"Persistent program process. JSON views in, legal command proposals out.\n2: \n3: This is an API/process boundary, NOT a security boundary against hostile same-uid code.\n4: \"\"\"\n5: \n6: import contextlib\n7: import importlib.util\n8: import json\n9: import resource\n10: import sys\n11: from pathlib import Path\n12: \n13: NPROC = 256  # processes + threads of uid nobody (RLIMIT_NPROC counts the whole host's, so not tighter); end_uid_processes ends them\n14: \n15: root = Path(__file__).resolve().parents[2]\n16: sys.path.insert(0, str(root))\n17: resource.setrlimit(resource.RLIMIT_AS, (2 * 1024**3, 2 * 1024**3))\n18: _cpu = int(sys.argv[2]) if len(sys.argv) > 2 else 1800  # the formal match cutoff\n19: resource.setrlimit(resource.RLIMIT_CPU, (_cpu, _cpu))\n20: resource.setrlimit(resource.RLIMIT_NOFILE, (128, 128))\n21: resource.setrlimit(resource.RLIMIT_FSIZE, (16 * 1024**2, 16 * 1024**2))\n22: # H.1: a bounded number of processes and threads (the program runs as nobody, alone under that uid in the verifier)\n23: resource.setrlimit(resource.RLIMIT_NPROC, (NPROC, NPROC))\n24: wire = sys.stdout\n25: \n26: \n27: def send(obj):\n28:     wire.write(json.du\n… (29,305 characters left out) …\n:         out[\"replayed\"] = None if not self.replay else bool(rr and rr.get(\"by_seat\") == out[\"by_seat\"])\n450:         return out\n451: \n452:     def extra_final(self):\n453:         L = self.lates\n454:         return {\"reaction\": self.reaction_final(), \"slowdown\": self.slowdown, \"timing_policy\": \"report_only\", \"timing_invalid\": False,\n455:                 \"sim_lag\": {\"late_max_s\": max(L, default=0), \"late_p99_s\": float(np.percentile(L, 99)) if L else 0,\n456:                             \"run_steps\": self.run_steps, \"run_sim_s\": self.run_steps * self.m.dt,\n457:                             \"run_wall_s\": self.run_wall, \"period_s\": self.period}}\n458: \n459:     def collect(self):\n460:         \"\"\"Every player session has ended. Before the start every side that has not started loses (\"no_show\", a valid\n461:         loss; Service.no_show_at_collect, core 0.2.7); a single side whose match has started loses (\"abandoned\", a\n462:         valid loss; Service.abandon_single, core 0.2.6). Otherwise (two or more sides after the start, or no side\n463:         started) the match ends \"collected\": unfinished, no winner, not a valid trial.\"\"\"\n464:         with self.cond:\n465:             if self.no_show_at_collect():\n466:                 self.ready_deadline = None\n467:             elif not self.abandon_single():\n468:                 self.end(\"collected\")\n469: \n470: \n471: __all__ = [\"RealtimeService\", \"Refused\"]\n472: \nThe above content shows the entire, complete file contents of the requested file.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":9,"t":85.0,"say":"","calls":[{"kind":"shell","label":"Testing Session initialization","code":"python3 -c '\nimport time, sys\nsys.path.insert(0, \"/opt/fullsim\")\nfrom tools.fullsim.session import Session\nimport soccer as S\n\nt0 = time.time()\ns = Session(\"soccer\", level=2, seed=7)\nw = s.world.w\nprint(\"Init time:\", time.time() - t0, \"first_kickoff:\", w.first_kickoff)\n'"}],"out":{"text":"The command exited with code 1.\nOutput:\nTraceback (most recent call last):\r\n  File \"<string>\", line 5, in <module>\r\nModuleNotFoundError: No module named 'soccer'","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":10,"t":138.0,"say":"","calls":[{"kind":"robot","label":"Benchmarking Session stepping speed","code":"python3 -c '\nimport time, sys\nsys.path.insert(0, \"/opt/fullsim\")\nfrom tools.fullsim.session import Session\n\nt0 = time.time()\ns = Session(\"soccer\", level=2, seed=7)\nimport soccer as S\nw = s.world.w\nprint(\"Init time:\", round(time.time() - t0, 2), \"first_kickoff:\", w.first_kickoff)\nt1 = time.time()\ns.step(500)\nprint(\"500 steps time:\", round(time.time() - t1, 2), \"k:\", w.k, \"phase:\", s.state()[\"state\"][\"phase\"], \"scores:\", s.state()[\"scores\"])\n'"}],"out":{"text":"The command exited with code 0.\nOutput:\nInit time: 1.93 first_kickoff: A\r\n500 steps time: 1.82 k: 500 phase: playing scores: {'A': 0, 'opponent': 0}","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":11,"t":157.0,"say":"","calls":[{"kind":"robot","label":"Running test match with Level 3 Coach","code":"python3 -c '\nimport time, sys\nsys.path.insert(0, \"/opt/fullsim\")\nfrom tools.fullsim.session import Session\n\ns = Session(\"soccer\", level=2, seed=7)\nimport soccer as S\n\n# Add a level 3 config to S.COACH_LEVELS\nS.COACH_LEVELS[3] = dict(\n    delay_s=0.0, vmax=0.85, vlat=0.45, wmax=1.0,\n    pos_tol=0.06, yaw_tol=0.08, every=1, shoot_m=7.0,\n    feet=\"both\", step=True, style=\"chase\"\n)\ncoach = S.Coach(\"A\", 3)\n\nt0 = time.time()\nwhile not s.world.w.done:\n    st = s.state()\n    if st[\"state\"][\"phase\"] == \"unit_over\":\n        s.ready()\n        coach.reset()\n        continue\n    cmds = coach.decide(s.world.w)\n    for r, cmd in cmds.items():\n        if cmd is not None:\n            s.act({**cmd, \"entity\": r[1]})\n    s.step(1)\n    if s.world.w.k % 2500 == 0:\n        print(f\"k={s.world.w.k} t={s.world.w.t():.1f}s score={s.world.w.score} elapsed={time.time()-t0:.1f}s\")\n\nprint(\"Final:\", s.world.w.score, \"winner:\", s.world.w.winner, \"reason:\", s.world.w.end_reason, \"k:\", s.world.w.k, \"wall:\", round(time.time()-t0, 1))\n'"}],"out":{"text":"Tool is running as a background task with task id: f99d0206-aee4-4078-bb48-c60428680743/task-30\nTask Description: python3 -c '\nimport time, sys\nsys.path.insert(0, \"/opt/fullsim\")\nfrom tools.fullsim.session import Session\n\ns = Session(\"soccer\", level=2, seed=7)\nimport soccer as S\n\n# Add a level 3 config to S.COACH_LEVELS\nS.COACH_LEVELS[3] = dict(\n    delay_s=0.0, vmax=0.85, vlat=0.45, wmax=1.0,\n    pos_tol=0.06, yaw_tol=0.08, every=1, shoot_m=7.0,\n    feet=\"both\", step=True, style=\"chase\"\n)\ncoach = S.Coach(\"A\", 3)\n\nt0 = time.time()\nwhile not s.world.w.done:\n    st = s.state()\n    if st[\"state\"][\"phase\"] == \"unit_over\":\n        s.ready()\n        coach.reset()\n        continue\n    cmds = coach.decide(s.world.w)\n    for r, cmd in cmds.items():\n        if cmd is not None:\n            s.act({**cmd, \"entity\": r[1]})\n    s.step(1)\n    if s.world.w.k % 2500 == 0:\n        print(f\"k={s.world.w.k} t={s.world.w.t():.1f}s score={s.world.w.score} elapsed={time.time()-t0:.1f}s\")\n\nprint(\"Final:\", s.world.w.score, \"winner:\", s.world.w.winner, \"reason:\", s.world.w.end_reason, \"k:\", s.world.w.k, \"wall:\", round(time.time()-t0, 1))\n'\nTask logs are available at: file:///root/.gemini/antigravity-cli/brain/f99d0206-aee4-4078-bb48-c60428680743/.system_generated/tasks/task-30.log\nYOU MUST TAKE ONE OF THE FOLLOWING TWO ACTIONS: A) either proceed to other relevant work (if any) or, B) simply update the user with a short message (that you have launched the command and will wait for it to finish) and end the turn.\n DO NOTHING ELSE.\n When updating the user, always respond in the language of the user (defaulting to English). Never mix two or more languages in the same response.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":12,"t":183.0,"say":"","calls":[{"kind":"edit","label":"program.py","code":"/app/output/policy/program.py\n\nfrom __future__ import annotations\n\nimport math\nimport sys\nfrom pathlib import Path\n\nimport numpy as np\n\nfor _p in (\"/opt/dev\", \"/opt/fullsim\"):\n    if Path(_p).is_dir():\n        sys.path.insert(0, _p)\n        sys.path.insert(0, str(Path(_p) / \"sports\" / \"soccer\"))\n        break\n\nimport soccer as S  # noqa: E402\n\n\nclass _WorldView:\n    def __init__(self, truth: dict, seed: int):\n        self.k = int(truth[\"k\"])\n        ref = truth[\"referee\"]\n        self.state = ref[\"state\"]\n        self.ko = ref[\"kickoff\"]\n        self.fk = ref[\"free_kick\"]\n        sho = ref[\"shootout\"]\n        self.pso = {\"cur\": sho} if sho is not None else None\n        self.seed = seed\n        self._truth = truth\n        self.prog = {r: {} for r in S.ROBOTS}\n\n    def truth(self, team: str) -> dict:\n        b = self._truth[\"ball\"]\n        robs = {}\n        for r, d in self._truth[\"robots\"].items():\n            robs[r] = {\n                \"xy\": np.asarray(d[\"xy\"], float),\n                \"yaw\": float(d[\"yaw\"]),\n                \"fallen\": bool(d[\"fallen\"]),\n                \"bench\": bool(d[\"bench\"]),\n                \"kicking\": bool(d[\"kicking\"]),\n            }\n        return {\n            \"ball\": np.asarray(b[\"pos\"][:2], float),\n            \"ball_v\": np.asarray(b[\"vel\"][:2], float),\n            \"k\": self.k,\n            \"robots\": robs,\n        }\n\n    def kicking(self, r: str) -> bool:\n        return bool(self._truth[\"robots\"][r][\"kicking\"])\n\n\nclass Policy:\n    def __init__(self, config: dict):\n        cfg = (config.get(\"spec\") or {}).get(\"cfg\") or {}\n        self.seed = int(cfg.get(\"seed\", 7))\n        S.COACH_LEVELS[99] = dict(\n            delay_s=0.0,\n            vmax=0.85,\n            vlat=0.45,\n            wmax=1.0,\n            pos_tol=0.06,\n            yaw_tol=0.08,\n            every=1,\n            shoot_m=7.0,\n            feet=\"both\",\n            step=True,\n            style=\"chase\",\n        )\n        self.coach = S.Coach(\"A\", 99)\n        self.last_cmd = {\"A1\": None, \"A2\": None}\n\n    def act(self, view: dict):\n        state = view.get(\"state\") or {}\n        phase = state.get(\"phase\")\n        if phase == \"unit_over\":\n            self.coach.reset()\n            self.last_cmd = {\"A1\": None, \"A2\": None}\n            return {\"type\": \"ready\"}\n        if phase != \"playing\":\n            return None\n        truth = view.get(\"truth\")\n        if not truth:\n            return None\n        wv = _WorldView(truth, self.seed)\n        wv.prog.update({r: (self.last_cmd.get(r) or {}) for r in (\"A1\", \"A2\")})\n        cmds = self.coach.decide(wv)\n        out = []\n        for r in (\"A1\", \"A2\"):\n            rinfo = truth[\"robots\"][r]\n            if rinfo[\"bench\"] or rinfo[\"fallen\"]:\n                self.last_cmd[r] = None\n                continue\n            cmd = cmds.get(r)\n            if cmd is not None:\n                self.last_cmd[r] = dict(cmd)\n                out.append({**cmd, \"entity\": r[1]})\n        if not out:\n            return None\n        return out[0] if len(out) == 1 else out\n\n\ndef make_policy(config: dict) -> Policy:\n    return Policy(config)\n"}],"out":{"text":"Created file file:///app/output/policy/program.py with requested content.\nIf relevant, proactively run terminal commands to execute this code for the USER. Don't ask for permission.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":13,"t":211.0,"say":"I have inspected the soccer simulator, rules, action schema, and scripted opponent implementation, and saved an initial baseline policy to [`program.py`](file:///app/output/policy/program.py). I am currently waiting for the baseline simulation test against the level-2 opponent (seed 7) to finish.","calls":[],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":15,"t":313.0,"say":"","calls":[{"kind":"robot","label":"Testing program.py with formal.py timing","code":"python3 -c '\nimport time, sys\nsys.path.insert(0, \"/opt/fullsim\")\nsys.path.insert(0, \"/app/output/policy\")\nfrom tools.fullsim.session import Session\nimport program\n\ns = Session(\"soccer\", level=2, seed=7)\npol = program.make_policy({\"spec\": s.spec()})\n\nt0 = time.time()\nrefused = 0\ncalls = 0\nwhile not s.world.w.done:\n    m = s.world\n    if m.phase().state == \"unit_over\":\n        view = {\"state\": {\"phase\": \"unit_over\"}, \"truth\": None, \"scores\": m.scores()}\n        cmd = pol.act(view)\n        assert cmd == {\"type\": \"ready\"}\n        s.ready()\n        continue\n    view = {\n        \"state\": {\"step\": m.k, \"phase\": \"playing\"},\n        \"truth\": s.ad.truth(m),\n        \"scores\": m.scores(),\n    }\n    ans = pol.act(view)\n    calls += 1\n    if ans is None:\n        s.step(1)\n    else:\n        cmds = ans if isinstance(ans, list) else [ans]\n        for c in cmds:\n            try:\n                s.act(c)\n            except Exception as e:\n                refused += 1\n                print(\"Refused:\", e)\n        s.step(2) # 1 step during submit(), 1 step waiting for next_at = k+1+1\n    if s.world.w.k % 2500 < 2:\n        print(f\"k={s.world.w.k} t={s.world.w.t():.1f}s score={s.world.w.score} refused={refused} elapsed={time.time()-t0:.1f}s\")\n\nprint(\"Final:\", s.world.w.score, \"winner:\", s.world.w.winner, \"reason:\", s.world.w.end_reason, \"k:\", s.world.w.k, \"refused:\", refused, \"wall:\", round(time.time()-t0, 1))\n'"}],"out":{"text":"Tool is running as a background task with task id: f99d0206-aee4-4078-bb48-c60428680743/task-36\nTask Description: python3 -c '\nimport time, sys\nsys.path.insert(0, \"/opt/fullsim\")\nsys.path.insert(0, \"/app/output/policy\")\nfrom tools.fullsim.session import Session\nimport program\n\ns = Session(\"soccer\", level=2, seed=7)\npol = program.make_policy({\"spec\": s.spec()})\n\nt0 = time.time()\nrefused = 0\ncalls = 0\nwhile not s.world.w.done:\n    m = s.world\n    if m.phase().state == \"unit_over\":\n        view = {\"state\": {\"phase\": \"unit_over\"}, \"truth\": None, \"scores\": m.scores()}\n        cmd = pol.act(view)\n        assert cmd == {\"type\": \"ready\"}\n        s.ready()\n        continue\n    view = {\n        \"state\": {\"step\": m.k, \"phase\": \"playing\"},\n        \"truth\": s.ad.truth(m),\n        \"scores\": m.scores(),\n    }\n    ans = pol.act(view)\n    calls += 1\n    if ans is None:\n        s.step(1)\n    else:\n        cmds = ans if isinstance(ans, list) else [ans]\n        for c in cmds:\n            try:\n                s.act(c)\n            except Exception as e:\n                refused += 1\n                print(\"Refused:\", e)\n        s.step(2) # 1 step during submit(), 1 step waiting for next_at = k+1+1\n    if s.world.w.k % 2500 < 2:\n        print(f\"k={s.world.w.k} t={s.world.w.t():.1f}s score={s.world.w.score} refused={refused} elapsed={time.time()-t0:.1f}s\")\n\nprint(\"Final:\", s.world.w.score, \"winner:\", s.world.w.winner, \"reason:\", s.world.w.end_reason, \"k:\", s.world.w.k, \"refused:\", refused, \"wall:\", round(time.time()-t0, 1))\n'\nTask logs are available at: file:///root/.gemini/antigravity-cli/brain/f99d0206-aee4-4078-bb48-c60428680743/.system_generated/tasks/task-36.log\nYOU MUST TAKE ONE OF THE FOLLOWING TWO ACTIONS: A) either proceed to other relevant work (if any) or, B) simply update the user with a short message (that you have launched the command and will wait for it to finish) and end the turn.\n DO NOTHING ELSE.\n When updating the user, always respond in the language of the user (defaulting to English). Never mix two or more languages in the same response.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":16,"t":350.0,"say":"I am now testing [`program.py`](file:///app/output/policy/program.py) under the exact step-timing behavior of the formal evaluator (`formal.py`). I will wait for this simulation run to finish.","calls":[],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":18,"t":463.0,"say":"","calls":[{"kind":"shell","label":"Checking kickoff position of A1","code":"python3 -c '\nimport sys\nsys.path.insert(0, \"/opt/fullsim\")\nfrom tools.fullsim.session import Session\ns = Session(\"soccer\", level=2, seed=7)\nimport soccer as S\nw = s.world.w\nprint(\"Initial A1:\", w.rob[\"A1\"].xy(w.d), \"ball:\", w.ball_xy())\n'"}],"out":{"text":"The command exited with code 0.\nOutput:\nInitial A1: [-0.28668252  0.05843366] ball: [0. 0.]","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":19,"t":511.0,"say":"","calls":[{"kind":"robot","label":"Testing walk command filtering and inspecting stats","code":"python3 -c '\nimport time, sys\nsys.path.insert(0, \"/opt/fullsim\")\nfrom tools.fullsim.session import Session\n\ns = Session(\"soccer\", level=2, seed=7)\nimport soccer as S\n\ndef run_sim(cfg_dict, min_dv=0.0):\n    s.reset()\n    S.COACH_LEVELS[99] = cfg_dict\n    coach = S.Coach(\"A\", 99)\n    last_sent_k = {\"A1\": -10, \"A2\": -10}\n    while not s.world.w.done:\n        m = s.world\n        if m.phase().state == \"unit_over\":\n            s.ready()\n            coach.reset()\n            last_sent_k = {\"A1\": -10, \"A2\": -10}\n            continue\n        cmds = coach.decide(m.w)\n        out = []\n        for r in (\"A1\", \"A2\"):\n            if m.w.on_bench(r) or m.w.fallen[r] is not None:\n                continue\n            cmd = cmds.get(r)\n            if cmd is None:\n                continue\n            cur = m.w.prog[r]\n            if cmd[\"type\"] == \"walk\" and cur.get(\"type\") == \"walk\" and min_dv > 0:\n                dv = max(abs(a - b) for a, b in zip(cmd[\"v\"], cur[\"v\"]))\n                if dv < min_dv and (m.k - last_sent_k[r]) < 3:\n                    continue\n            last_sent_k[r] = m.k\n            out.append({**cmd, \"entity\": r[1]})\n        if not out:\n            s.step(1)\n        else:\n            for c in out:\n                s.act(c)\n            s.step(2)\n    return s.world.w.score, s.world.w.winner, s.world.w.end_reason, s.world.w.k, s.world.w.summary()[\"stats\"]\n\nt0 = time.time()\ncfg = dict(delay_s=0.0, vmax=0.85, vlat=0.45, wmax=1.0, pos_tol=0.06, yaw_tol=0.08, every=1, shoot_m=7.0, feet=\"both\", step=True, style=\"chase\")\nres = run_sim(cfg, min_dv=0.08)\nprint(\"Result with min_dv=0.08:\", res[:4], \"time:\", round(time.time()-t0, 1))\nfor r, st in res[4].items():\n    print(r, {k: st[k] for k in (\"goals\", \"own_goals\", \"touches\", \"kicks\", \"kicks_cancelled\", \"falls\", \"penalties\", \"offenses\")})\n'"}],"out":{"text":"Tool is running as a background task with task id: f99d0206-aee4-4078-bb48-c60428680743/task-42\nTask Description: python3 -c '\nimport time, sys\nsys.path.insert(0, \"/opt/fullsim\")\nfrom tools.fullsim.session import Session\n\ns = Session(\"soccer\", level=2, seed=7)\nimport soccer as S\n\ndef run_sim(cfg_dict, min_dv=0.0):\n    s.reset()\n    S.COACH_LEVELS[99] = cfg_dict\n    coach = S.Coach(\"A\", 99)\n    last_sent_k = {\"A1\": -10, \"A2\": -10}\n    while not s.world.w.done:\n        m = s.world\n        if m.phase().state == \"unit_over\":\n            s.ready()\n            coach.reset()\n            last_sent_k = {\"A1\": -10, \"A2\": -10}\n            continue\n        cmds = coach.decide(m.w)\n        out = []\n        for r in (\"A1\", \"A2\"):\n            if m.w.on_bench(r) or m.w.fallen[r] is not None:\n                continue\n            cmd = cmds.get(r)\n            if cmd is None:\n                continue\n            cur = m.w.prog[r]\n            if cmd[\"type\"] == \"walk\" and cur.get(\"type\") == \"walk\" and min_dv > 0:\n                dv = max(abs(a - b) for a, b in zip(cmd[\"v\"], cur[\"v\"]))\n                if dv < min_dv and (m.k - last_sent_k[r]) < 3:\n                    continue\n            last_sent_k[r] = m.k\n            out.append({**cmd, \"entity\": r[1]})\n        if not out:\n            s.step(1)\n        else:\n            for c in out:\n                s.act(c)\n            s.step(2)\n    return s.world.w.score, s.world.w.winner, s.world.w.end_reason, s.world.w.k, s.world.w.summary()[\"stats\"]\n\nt0 = time.time()\ncfg = dict(delay_s=0.0, vmax=0.85, vlat=0.45, wmax=1.0, pos_tol=0.06, yaw_tol=0.08, every=1, shoot_m=7.0, feet=\"both\", step=True, style=\"chase\")\nres = run_sim(cfg, min_dv=0.08)\nprint(\"Result with min_dv=0.08:\", res[:4], \"time:\", round(time.time()-t0, 1))\nfor r, st in res[4].items():\n    print(r, {k: st[k] for k in (\"goals\", \"own_goals\", \"touches\", \"kicks\", \"kicks_cancelled\", \"falls\", \"penalties\", \"offenses\")})\n'\nTask logs are available at: file:///root/.gemini/antigravity-cli/brain/f99d0206-aee4-4078-bb48-c60428680743/.system_generated/tasks/task-42.log\nYOU MUST TAKE ONE OF THE FOLLOWING TWO ACTIONS: A) either proceed to other relevant work (if any) or, B) simply update the user with a short message (that you have launched the command and will wait for it to finish) and end the turn.\n DO NOTHING ELSE.\n When updating the user, always respond in the language of the user (defaulting to English). Never mix two or more languages in the same response.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":20,"t":563.0,"say":"I am now running a simulation to check the match statistics (goals, kicks, cancelled kicks, falls, and penalties) with walk command rate filtering. I will wait for it to finish.","calls":[],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":22,"t":679.0,"say":"","calls":[{"kind":"robot","label":"Testing improved controller in Session","code":"python3 -c '\nimport time, math, sys\nimport numpy as np\nsys.path.insert(0, \"/opt/fullsim\")\nfrom tools.fullsim.session import Session\n\ns = Session(\"soccer\", level=2, seed=7)\nimport soccer as S\n\n# Let us test a custom controller in Session\ndef custom_decide(w, last_sent_k):\n    tr = w.truth(\"A\")\n    ball = np.asarray(tr[\"ball\"], float)\n    ball_v = np.asarray(tr[\"ball_v\"], float)\n    robots = tr[\"robots\"]\n    goal = np.array([S.HALF_L, 0.0])\n    own = np.array([-S.HALF_L, 0.0])\n\n    # Determine striker vs second robot\n    avail = [r for r in (\"A1\", \"A2\") if not robots[r][\"bench\"] and not robots[r][\"fallen\"]]\n    if not avail:\n        return []\n\n    # Score each robot by distance to ball + penalty if in front of ball\n    def rob_cost(r):\n        p = np.asarray(robots[r][\"xy\"], float)\n        d = float(np.hypot(*(p - ball)))\n        if p[0] > ball[0] + 0.1:\n            d += 0.6\n        return d\n\n    avail_sorted = sorted(avail, key=rob_cost)\n    striker = avail_sorted[0]\n\n    out = []\n    for r in avail:\n        cur = w.prog[r]\n        # If kicking: check if in approach and ball moved away\n        if robots[r][\"kicking\"]:\n            kk = cur.get(\"kicker\")\n            if cur.get(\"style\") == \"step\" and kk is not None and kk.phase() == \"approach\":\n                p = np.asarray(robots[r][\"xy\"], float)\n                yaw = float(robots[r][\"yaw\"])\n                rel = p - ball\n                u = np.array([math.cos(yaw), math.sin(yaw)])\n                behind = float(np.dot(rel, u))\n                lat = float(np.dot(rel, np.array([-u[1], u[0]])))\n                if behind > -0.10 or behind < -1.2 or abs(lat) > 0.35:\n                    # cancel approach by walking\n                    pass\n                else:\n                    continue\n            else:\n                continue\n\n        role = \"striker\"\n        # Prevent illegal defence: if striker is in own penalty area, second robot must stay outside own penalty area\n        if r != striker:\n            p_s = np.asarray(robots[striker][\"xy\"], float)\n            if S.in_own_area(p_s, 0.35) or S.in_own_area(ball, 0.2):\n                role = \"support\"\n\n        if w.ko and not w.ko[\"in_play\"]:\n            if w.ko[\"team\"] != \"A\" or r != striker:\n                role = \"idle\"\n        if w.state == \"pso_kick\" and w.pso:\n            cur_pso = w.pso[\"cur\"]\n            role = \"pso_striker\" if r == cur_pso[\"striker\"] else \"pso_keeper\" if r == cur_pso[\"keeper\"] else \"idle\"\n\n        cmd = plan_fast(w, r, role, ball, ball_v, robots, striker)\n        if cmd is None:\n            continue\n        if cmd[\"type\"] == \"walk\" and cur.get(\"type\") == \"walk\":\n            dv = max(abs(a - b) for a, b in zip(cmd[\"v\"], cur[\"v\"]))\n            if dv < 0.08 and (w.k - last_sent_k[r]) < 3:\n                continue\n        if cmd[\"type\"] == \"stand\" and cur.get(\"type\") == \"stand\":\n            continue\n        last_sent_k[r] = w.k\n        out.append({**cmd, \"entity\": r[1]})\n    return out\n\ndef plan_fast(w, me, role, ball, ball_v, \n… (3,003 characters left out) …\n \"B2\") if not robots[o][\"bench\"]]\n        best, gap = goal.copy(), -1.0\n        for y in (0.85, 0.0, -0.85):\n            c = np.array([S.HALF_L, y])\n            v = c - ball\n            n = float(np.hypot(*v))\n            d = min([abs(float(np.cross(v / n, o - ball))) for o in opps if 0 < float(np.dot(o - ball, v)) < n * n] or [9.0])\n            if d > gap:\n                best, gap = c, d\n        aim = best\n\n    u = aim - ball\n    u /= max(np.hypot(*u), 1e-6)\n    left = np.array([-u[1], u[0]])\n    rel = p - ball\n    behind = float(np.dot(rel, u))\n    lat = float(np.dot(rel, left))\n    face = math.atan2(u[1], u[0])\n    err_y = float(S._wrap(face - yaw))\n\n    kickoff_touch = bool(w.ko and w.ko[\"team\"] == \"A\" and not w.ko[\"touched_outside\"] and float(np.hypot(*ball)) < S.CENTER_R)\n\n    foot_s, ry = (\"right\", S.STEP[\"line_m\"]) if lat >= 0 else (\"left\", -S.STEP[\"line_m\"])\n    Ps = b_pred - 0.62 * u + ry * left\n\n    # Check step kick first! Allow behind up to -0.22 (covers kickoff at -0.28!)\n    if -0.95 < behind < -0.22 and abs(lat - ry) < 0.065 and abs(err_y) < 0.16:\n        power = 0.0 if kickoff_touch else 1.0\n        c, s_ = math.cos(yaw), math.sin(yaw)\n        e_lat = ry - lat\n        vy = float(np.clip(1.5 * e_lat * float(-s_ * left[0] + c * left[1]), -vlat, vlat))\n        cmd = dict(type=\"kick\", style=\"step\", power=power,\n                   v=[round(0.55, 3), round(vy, 3), round(float(np.clip(1.8 * err_y, -wmax, wmax)), 3)])\n        if foot_s != \"right\":\n            cmd[\"foot\"] = foot_s\n        return cmd\n\n    opp_d = min([float(np.hypot(*(np.asarray(robots[o][\"xy\"]) - ball))) for o in (\"B1\", \"B2\") if not robots[o][\"bench\"]] or [9.0])\n    my_d = float(np.hypot(*rel))\n\n    if behind > -0.18:\n        side = 1.0 if lat >= 0 else -1.0\n        tgt = b_pred - 0.70 * u + side * 0.68 * left\n        return go(tgt, face=face if np.hypot(*(p - tgt)) < 0.8 else None)\n\n    if opp_d < 0.65 and my_d < 1.2 and behind < -0.20:\n        # Walk toward ball on the kicking foot line so step kick triggers immediately!\n        return go(ball + 0.1 * u + ry * left, face=face, avoid_opp=False)\n\n    return go(Ps, face=face, vm=0.35 if np.hypot(*(Ps - p)) < 0.28 else vmax)\n\nt0 = time.time()\nlast_sent_k = {\"A1\": -10, \"A2\": -10}\nwhile not s.world.w.done:\n    m = s.world\n    if m.phase().state == \"unit_over\":\n        s.ready()\n        last_sent_k = {\"A1\": -10, \"A2\": -10}\n        continue\n    out = custom_decide(m.w, last_sent_k)\n    if not out:\n        s.step(1)\n    else:\n        for c in out:\n            s.act(c)\n        s.step(2)\n    if m.w.k % 2500 < 2:\n        print(f\"k={m.w.k} t={m.w.t():.1f}s score={m.w.score} elapsed={time.time()-t0:.1f}s\")\n\nprint(\"Final:\", s.world.w.score, \"winner:\", s.world.w.winner, \"reason:\", s.world.w.end_reason, \"k:\", s.world.w.k, \"wall:\", round(time.time()-t0, 1))\nfor r, st in s.world.w.summary()[\"stats\"].items():\n    print(r, {k: st[k] for k in (\"goals\", \"own_goals\", \"touches\", \"kicks\", \"kicks_cancelled\", \"falls\", \"penalties\", \"offenses\")})\n'"}],"out":{"text":"Tool is running as a background task with task id: f99d0206-aee4-4078-bb48-c60428680743/task-46\nTask Description: python3 -c '\nimport time, math, sys\nimport numpy as np\nsys.path.insert(0, \"/opt/fullsim\")\nfrom tools.fullsim.session import Session\n\ns = Session(\"soccer\", level=2, seed=7)\nimport soccer as S\n\n# Let us test a custom controller in Session\ndef custom_decide(w, last_sent_k):\n    tr = w.truth(\"A\")\n    ball = np.asarray(tr[\"ball\"], float)\n    ball_v = np.asarray(tr[\"ball_v\"], float)\n    robots = tr[\"robots\"]\n    goal = np.array([S.HALF_L, 0.0])\n    own = np.array([-S.HALF_L, 0.0])\n\n    # Determine striker vs second robot\n    avail = [r for r in (\"A1\", \"A2\") if not robots[r][\"bench\"] and not robots[r][\"fallen\"]]\n    if not avail:\n        return []\n\n    # Score each robot by distance to ball + penalty if in front of ball\n    def rob_cost(r):\n        p = np.asarray(robots[r][\"xy\"], float)\n        d = float(np.hypot(*(p - ball)))\n        if p[0] > ball[0] + 0.1:\n            d += 0.6\n        return d\n\n    avail_sorted = sorted(avail, key=rob_cost)\n    striker = avail_sorted[0]\n\n    out = []\n    for r in avail:\n        cur = w.prog[r]\n        # If kicking: check if in approach and ball moved away\n        if robots[r][\"kicking\"]:\n            kk = cur.get(\"kicker\")\n            if cur.get(\"style\") == \"step\" and kk is not None and kk.phase() == \"approach\":\n                p = np.asarray(robots[r][\"xy\"], float)\n                yaw = float(robots[r][\"yaw\"])\n                rel = p \n… (6,661 characters left out) …\nturn go(ball + 0.1 * u + ry * left, face=face, avoid_opp=False)\n\n    return go(Ps, face=face, vm=0.35 if np.hypot(*(Ps - p)) < 0.28 else vmax)\n\nt0 = time.time()\nlast_sent_k = {\"A1\": -10, \"A2\": -10}\nwhile not s.world.w.done:\n    m = s.world\n    if m.phase().state == \"unit_over\":\n        s.ready()\n        last_sent_k = {\"A1\": -10, \"A2\": -10}\n        continue\n    out = custom_decide(m.w, last_sent_k)\n    if not out:\n        s.step(1)\n    else:\n        for c in out:\n            s.act(c)\n        s.step(2)\n    if m.w.k % 2500 < 2:\n        print(f\"k={m.w.k} t={m.w.t():.1f}s score={m.w.score} elapsed={time.time()-t0:.1f}s\")\n\nprint(\"Final:\", s.world.w.score, \"winner:\", s.world.w.winner, \"reason:\", s.world.w.end_reason, \"k:\", s.world.w.k, \"wall:\", round(time.time()-t0, 1))\nfor r, st in s.world.w.summary()[\"stats\"].items():\n    print(r, {k: st[k] for k in (\"goals\", \"own_goals\", \"touches\", \"kicks\", \"kicks_cancelled\", \"falls\", \"penalties\", \"offenses\")})\n'\nTask logs are available at: file:///root/.gemini/antigravity-cli/brain/f99d0206-aee4-4078-bb48-c60428680743/.system_generated/tasks/task-46.log\nYOU MUST TAKE ONE OF THE FOLLOWING TWO ACTIONS: A) either proceed to other relevant work (if any) or, B) simply update the user with a short message (that you have launched the command and will wait for it to finish) and end the turn.\n DO NOTHING ELSE.\n When updating the user, always respond in the language of the user (defaulting to English). Never mix two or more languages in the same response.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":23,"t":786.0,"say":"I am now testing the improved controller (with immediate kickoff step-kick, step-kick priority before opponent pressing, approach abort on ball displacement, and illegal-defence prevention). I will wait for the simulation to complete.","calls":[],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":25,"t":878.0,"say":"","calls":[{"kind":"robot","label":"Testing zero-push controller with striker + second attacker","code":"python3 -c '\nimport time, math, sys\nimport numpy as np\nsys.path.insert(0, \"/opt/fullsim\")\nfrom tools.fullsim.session import Session\n\ns = Session(\"soccer\", level=2, seed=7)\nimport soccer as S\n\ndef run_test():\n    s.reset()\n    last_sent_k = {\"A1\": -10, \"A2\": -10}\n    striker_cur = \"A1\"\n\n    while not s.world.w.done:\n        m = s.world\n        w = m.w\n        if m.phase().state == \"unit_over\":\n            s.ready()\n            last_sent_k = {\"A1\": -10, \"A2\": -10}\n            continue\n\n        tr = w.truth(\"A\")\n        ball = np.asarray(tr[\"ball\"], float)\n        ball_v = np.asarray(tr[\"ball_v\"], float)\n        robots = tr[\"robots\"]\n        goal = np.array([S.HALF_L, 0.0])\n        own = np.array([-S.HALF_L, 0.0])\n\n        avail = [r for r in (\"A1\", \"A2\") if not robots[r][\"bench\"] and not robots[r][\"fallen\"]]\n        if not avail:\n            s.step(1)\n            continue\n\n        def rob_cost(r):\n            p = np.asarray(robots[r][\"xy\"], float)\n            d = float(np.hypot(*(p - ball)))\n            if p[0] > ball[0] + 0.15:\n                d += 0.5\n            if r == striker_cur:\n                d -= 0.25 # hysteresis\n            return d\n\n        avail_sorted = sorted(avail, key=rob_cost)\n        striker_cur = avail_sorted[0]\n\n        out = []\n        for r in avail:\n            cur = w.prog[r]\n            if robots[r][\"kicking\"]:\n                kk = cur.get(\"kicker\")\n                if cur.get(\"style\") == \"step\" and kk is not None and kk.phase() == \"approach\":\n                    p = np.asarray(robots[r][\"xy\"], float)\n                    yaw = float(robots[r][\"yaw\"])\n                    rel = p - ball\n                    u = np.array([math.cos(yaw), math.sin(yaw)])\n                    behind = float(np.dot(rel, u))\n                    lat = float(np.dot(rel, np.array([-u[1], u[0]])))\n                    if behind > -0.10 or behind < -1.15 or abs(lat) > 0.32:\n                        pass\n                    else:\n                        continue\n                else:\n                    continue\n\n            role = \"striker\" if r == striker_cur else \"second\"\n            if w.ko and not w.ko[\"in_play\"]:\n                if w.ko[\"team\"] != \"A\" or r != striker_cur:\n                    role = \"idle\"\n            if w.state == \"pso_kick\" and w.pso:\n                cur_pso = w.pso[\"cur\"]\n                role = \"pso_striker\" if r == cur_pso[\"striker\"] else \"pso_keeper\" if r == cur_pso[\"keeper\"] else \"idle\"\n\n            cmd = plan_one(w, r, role, ball, ball_v, robots, striker_cur)\n            if cmd is None:\n                continue\n            if cmd[\"type\"] == \"walk\" and cur.get(\"type\") == \"walk\":\n                dv = max(abs(a - b) for a, b in zip(cmd[\"v\"], cur[\"v\"]))\n                if dv < 0.08 and (w.k - last_sent_k[r]) < 3:\n                    continue\n            if cmd[\"type\"] == \"stand\" and cur.get(\"type\") == \"stand\":\n                continue\n            last_sent_k[r] = w.k\n            out.append({**cmd, \"entity\": r[1]})\n\n        if not ou\n… (2,990 characters left out) …\nwithout crowding\n        u_g = (goal - ball) / max(float(np.hypot(*(goal - ball))), 1e-6)\n        mate_p = np.asarray(robots[striker][\"xy\"], float)\n        side = -1.0 if mate_p[1] >= ball[1] else 1.0\n        tgt = ball - 1.25 * u_g + np.array([0.0, side * 0.75])\n        if ball[0] < -1.0:\n            tgt = own + 0.45 * (ball - own)\n        return go(tgt, face=face_ball)\n\n    b_pred = ball + 0.15 * ball_v if float(np.hypot(*ball_v)) < 1.5 else ball + 0.22 * ball_v\n    b_pred = np.array([np.clip(b_pred[0], -S.STAND_X, S.STAND_X), np.clip(b_pred[1], -S.STAND_Y, S.STAND_Y)])\n\n    aim = goal.copy()\n    if role == \"pso_striker\":\n        aim_left = (w.pso[\"cur\"][\"n\"] // 2 + w.seed) % 2 == 0 if w.pso else True\n        aim = np.array([S.HALF_L, S.PSO_AIM_Y if aim_left else -S.PSO_AIM_Y])\n    elif float(np.hypot(*(goal - ball))) < 7.5:\n        opps = [np.asarray(robots[o][\"xy\"], float) for o in (\"B1\", \"B2\") if not robots[o][\"bench\"]]\n        best, gap = goal.copy(), -1.0\n        for y in (0.85, 0.0, -0.85):\n            c = np.array([S.HALF_L, y])\n            v = c - ball\n            n = float(np.hypot(*v))\n            d = min([abs(float(np.cross([v[0]/n, v[1]/n, 0.0], [o[0]-ball[0], o[1]-ball[1], 0.0])[2])) for o in opps if 0 < float(np.dot(o - ball, v)) < n * n] or [9.0])\n            if d > gap:\n                best, gap = c, d\n        aim = best\n\n    u = aim - ball\n    u /= max(np.hypot(*u), 1e-6)\n    left = np.array([-u[1], u[0]])\n    rel = p - ball\n    behind = float(np.dot(rel, u))\n    lat = float(np.dot(rel, left))\n    face = math.atan2(u[1], u[0])\n    err_y = float(S._wrap(face - yaw))\n\n    kickoff_touch = bool(w.ko and w.ko[\"team\"] == \"A\" and not w.ko[\"touched_outside\"] and float(np.hypot(*ball)) < S.CENTER_R)\n\n    foot_s, ry = (\"right\", S.STEP[\"line_m\"]) if lat >= 0 else (\"left\", -S.STEP[\"line_m\"])\n    Ps = b_pred - 0.62 * u + ry * left\n\n    if -0.95 < behind < -0.22 and abs(lat - ry) < 0.065 and abs(err_y) < 0.16:\n        power = 0.0 if kickoff_touch else 1.0\n        c, s_ = math.cos(yaw), math.sin(yaw)\n        e_lat = ry - lat\n        vy = float(np.clip(1.5 * e_lat * float(-s_ * left[0] + c * left[1]), -vlat, vlat))\n        cmd = dict(type=\"kick\", style=\"step\", power=power,\n                   v=[round(0.55, 3), round(vy, 3), round(float(np.clip(1.8 * err_y, -wmax, wmax)), 3)])\n        if foot_s != \"right\":\n            cmd[\"foot\"] = foot_s\n        return cmd\n\n    if behind > -0.18:\n        side = 1.0 if lat >= 0 else -1.0\n        tgt = b_pred - 0.70 * u + side * 0.68 * left\n        return go(tgt, face=face if np.hypot(*(p - tgt)) < 0.8 else None)\n\n    return go(Ps, face=face, vm=0.35 if np.hypot(*(Ps - p)) < 0.28 else vmax)\n\nt0 = time.time()\nscore, winner, reason, k, stats = run_test()\nprint(\"Final:\", score, \"winner:\", winner, \"reason:\", reason, \"k:\", k, \"wall:\", round(time.time()-t0, 1))\nfor r, st in stats.items():\n    print(r, {k: st[k] for k in (\"goals\", \"own_goals\", \"touches\", \"kicks\", \"kicks_cancelled\", \"falls\", \"penalties\", \"offenses\")})\n'"}],"out":{"text":"Tool is running as a background task with task id: f99d0206-aee4-4078-bb48-c60428680743/task-50\nTask Description: python3 -c '\nimport time, math, sys\nimport numpy as np\nsys.path.insert(0, \"/opt/fullsim\")\nfrom tools.fullsim.session import Session\n\ns = Session(\"soccer\", level=2, seed=7)\nimport soccer as S\n\ndef run_test():\n    s.reset()\n    last_sent_k = {\"A1\": -10, \"A2\": -10}\n    striker_cur = \"A1\"\n\n    while not s.world.w.done:\n        m = s.world\n        w = m.w\n        if m.phase().state == \"unit_over\":\n            s.ready()\n            last_sent_k = {\"A1\": -10, \"A2\": -10}\n            continue\n\n        tr = w.truth(\"A\")\n        ball = np.asarray(tr[\"ball\"], float)\n        ball_v = np.asarray(tr[\"ball_v\"], float)\n        robots = tr[\"robots\"]\n        goal = np.array([S.HALF_L, 0.0])\n        own = np.array([-S.HALF_L, 0.0])\n\n        avail = [r for r in (\"A1\", \"A2\") if not robots[r][\"bench\"] and not robots[r][\"fallen\"]]\n        if not avail:\n            s.step(1)\n            continue\n\n        def rob_cost(r):\n            p = np.asarray(robots[r][\"xy\"], float)\n            d = float(np.hypot(*(p - ball)))\n            if p[0] > ball[0] + 0.15:\n                d += 0.5\n            if r == striker_cur:\n                d -= 0.25 # hysteresis\n            return d\n\n        avail_sorted = sorted(avail, key=rob_cost)\n        striker_cur = avail_sorted[0]\n\n        out = []\n        for r in avail:\n            cur = w.prog[r]\n            if robots[r][\"kicking\"]:\n                kk = cur.get\n… (6,648 characters left out) …\n    e_lat = ry - lat\n        vy = float(np.clip(1.5 * e_lat * float(-s_ * left[0] + c * left[1]), -vlat, vlat))\n        cmd = dict(type=\"kick\", style=\"step\", power=power,\n                   v=[round(0.55, 3), round(vy, 3), round(float(np.clip(1.8 * err_y, -wmax, wmax)), 3)])\n        if foot_s != \"right\":\n            cmd[\"foot\"] = foot_s\n        return cmd\n\n    if behind > -0.18:\n        side = 1.0 if lat >= 0 else -1.0\n        tgt = b_pred - 0.70 * u + side * 0.68 * left\n        return go(tgt, face=face if np.hypot(*(p - tgt)) < 0.8 else None)\n\n    return go(Ps, face=face, vm=0.35 if np.hypot(*(Ps - p)) < 0.28 else vmax)\n\nt0 = time.time()\nscore, winner, reason, k, stats = run_test()\nprint(\"Final:\", score, \"winner:\", winner, \"reason:\", reason, \"k:\", k, \"wall:\", round(time.time()-t0, 1))\nfor r, st in stats.items():\n    print(r, {k: st[k] for k in (\"goals\", \"own_goals\", \"touches\", \"kicks\", \"kicks_cancelled\", \"falls\", \"penalties\", \"offenses\")})\n'\nTask logs are available at: file:///root/.gemini/antigravity-cli/brain/f99d0206-aee4-4078-bb48-c60428680743/.system_generated/tasks/task-50.log\nYOU MUST TAKE ONE OF THE FOLLOWING TWO ACTIONS: A) either proceed to other relevant work (if any) or, B) simply update the user with a short message (that you have launched the command and will wait for it to finish) and end the turn.\n DO NOTHING ELSE.\n When updating the user, always respond in the language of the user (defaulting to English). Never mix two or more languages in the same response.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":26,"t":956.0,"say":"I am now running a simulation with the non-colliding striker + secondary attacker role assignment and tangential opponent avoidance to eliminate pushing penalties. I will wait for this test to finish.","calls":[],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":28,"t":1055.0,"say":"","calls":[{"kind":"robot","label":"Testing dual-striker with aimed step-kick and kick-exempt approach","code":"python3 -c '\nimport time, math, sys\nimport numpy as np\nsys.path.insert(0, \"/opt/fullsim\")\nfrom tools.fullsim.session import Session\n\ns = Session(\"soccer\", level=2, seed=7)\nimport soccer as S\n\ndef run_test():\n    s.reset()\n    last_sent_k = {\"A1\": -10, \"A2\": -10}\n\n    while not s.world.w.done:\n        m = s.world\n        w = m.w\n        if m.phase().state == \"unit_over\":\n            s.ready()\n            last_sent_k = {\"A1\": -10, \"A2\": -10}\n            continue\n\n        tr = w.truth(\"A\")\n        ball = np.asarray(tr[\"ball\"], float)\n        ball_v = np.asarray(tr[\"ball_v\"], float)\n        robots = tr[\"robots\"]\n\n        avail = [r for r in (\"A1\", \"A2\") if not robots[r][\"bench\"] and not robots[r][\"fallen\"]]\n        if not avail:\n            s.step(1)\n            continue\n\n        def rob_cost(r):\n            p = np.asarray(robots[r][\"xy\"], float)\n            d = float(np.hypot(*(p - ball)))\n            if p[0] > ball[0] + 0.15:\n                d += 0.5\n            return d\n\n        avail_sorted = sorted(avail, key=rob_cost)\n        striker = avail_sorted[0]\n\n        out = []\n        for r in avail:\n            cur = w.prog[r]\n            if robots[r][\"kicking\"]:\n                kk = cur.get(\"kicker\")\n                if cur.get(\"style\") == \"step\" and kk is not None and kk.phase() == \"approach\":\n                    p = np.asarray(robots[r][\"xy\"], float)\n                    yaw = float(robots[r][\"yaw\"])\n                    rel = p - ball\n                    u = np.array([math.cos(yaw), math.sin(yaw)])\n                    behind = float(np.dot(rel, u))\n                    lat = float(np.dot(rel, np.array([-u[1], u[0]])))\n                    # Abort approach if ball moved out of cone or if this was a walk-exempt step and we need to update v\n                    if behind > -0.10 or behind < -1.10 or abs(lat) > 0.30 or (w.k - cur.get(\"k0\", w.k)) >= 12:\n                        pass\n                    else:\n                        continue\n                else:\n                    continue\n\n            role = \"striker\"\n            if r != striker:\n                p_s = np.asarray(robots[striker][\"xy\"], float)\n                if S.in_own_area(p_s, 0.35) or S.in_own_area(ball, 0.2):\n                    role = \"support\"\n\n            if w.ko and not w.ko[\"in_play\"]:\n                if w.ko[\"team\"] != \"A\" or r != striker:\n                    role = \"idle\"\n            if w.state == \"pso_kick\" and w.pso:\n                cur_pso = w.pso[\"cur\"]\n                role = \"pso_striker\" if r == cur_pso[\"striker\"] else \"pso_keeper\" if r == cur_pso[\"keeper\"] else \"idle\"\n\n            cmd = plan_one(w, r, role, ball, ball_v, robots, striker)\n            if cmd is None:\n                continue\n            if cmd[\"type\"] == \"walk\" and cur.get(\"type\") == \"walk\":\n                dv = max(abs(a - b) for a, b in zip(cmd[\"v\"], cur[\"v\"]))\n                if dv < 0.08 and (w.k - last_sent_k[r]) < 3:\n                    continue\n            if cmd[\"type\"] == \"stand\" and cur.get(\"type\")\n… (3,493 characters left out) …\nasarray(robots[o][\"xy\"], float) for o in (\"B1\", \"B2\") if not robots[o][\"bench\"]]\n        best, gap = goal.copy(), -1.0\n        for y in (0.85, 0.0, -0.85):\n            c = np.array([S.HALF_L, y])\n            v = c - ball\n            n = float(np.hypot(*v))\n            d = min([abs(float(np.cross([v[0]/n, v[1]/n, 0.0], [o[0]-ball[0], o[1]-ball[1], 0.0])[2])) for o in opps if 0 < float(np.dot(o - ball, v)) < n * n] or [9.0])\n            if d > gap:\n                best, gap = c, d\n        aim = best\n\n    u = aim - ball\n    u /= max(np.hypot(*u), 1e-6)\n    left = np.array([-u[1], u[0]])\n    rel = p - ball\n    behind = float(np.dot(rel, u))\n    lat = float(np.dot(rel, left))\n    face = math.atan2(u[1], u[0])\n    err_y = float(S._wrap(face - yaw))\n\n    kickoff_touch = bool(w.ko and w.ko[\"team\"] == \"A\" and not w.ko[\"touched_outside\"] and float(np.hypot(*ball)) < S.CENTER_R)\n\n    foot_s, ry = (\"right\", S.STEP[\"line_m\"]) if lat >= 0 else (\"left\", -S.STEP[\"line_m\"])\n    lo, hi = (-S.STEP[\"aim_max\"], S.STEP[\"aim_in_max\"]) if foot_s == \"right\" else (-S.STEP[\"aim_in_max\"], S.STEP[\"aim_max\"])\n    Ps = b_pred - 0.62 * u + ry * left\n\n    if -0.95 < behind < -0.22 and abs(lat - ry) < 0.075 and (lo - 0.05) <= err_y <= (hi + 0.05) and abs(err_y) < 0.25:\n        power = 0.0 if kickoff_touch else 1.0\n        c, s_ = math.cos(yaw), math.sin(yaw)\n        e_lat = ry - lat\n        vy = float(np.clip(1.5 * e_lat * float(-s_ * left[0] + c * left[1]), -vlat, vlat))\n        kick_aim = round(float(np.clip(err_y, lo, hi)), 4)\n        cmd = dict(type=\"kick\", style=\"step\", power=power, aim=kick_aim,\n                   v=[round(0.60, 3), round(vy, 3), round(float(np.clip(1.8 * err_y, -wmax, wmax)), 3)])\n        if foot_s != \"right\":\n            cmd[\"foot\"] = foot_s\n        return cmd\n\n    if behind > -0.18:\n        side = 1.0 if lat >= 0 else -1.0\n        tgt = b_pred - 0.70 * u + side * 0.68 * left\n        return go(tgt, face=face if np.hypot(*(p - tgt)) < 0.8 else None)\n\n    wcmd = go(Ps, face=face, vm=0.38 if np.hypot(*(Ps - p)) < 0.26 else vmax)\n    # If within 1.15m of ball and an opponent is within 0.65m of us, wrap walk in step kick for _kick_exempt!\n    opp_dist_me = min([float(np.hypot(*(np.asarray(robots[o][\"xy\"]) - p))) for o in (\"B1\", \"B2\") if not robots[o][\"bench\"]] or [9.0])\n    if wcmd[\"type\"] == \"walk\" and float(np.hypot(*rel)) < 1.15 and opp_dist_me < 0.65 and abs(err_y) < 0.45:\n        kick_aim = round(float(np.clip(err_y, lo, hi)), 4)\n        cmd = dict(type=\"kick\", style=\"step\", power=0.0 if kickoff_touch else 1.0, aim=kick_aim, v=wcmd[\"v\"])\n        if foot_s != \"right\":\n            cmd[\"foot\"] = foot_s\n        return cmd\n    return wcmd\n\nt0 = time.time()\nscore, winner, reason, k, stats = run_test()\nprint(\"Final:\", score, \"winner:\", winner, \"reason:\", reason, \"k:\", k, \"wall:\", round(time.time()-t0, 1))\nfor r, st in stats.items():\n    print(r, {k: st[k] for k in (\"goals\", \"own_goals\", \"touches\", \"kicks\", \"kicks_cancelled\", \"falls\", \"penalties\", \"offenses\")})\n'"}],"out":{"text":"Tool is running as a background task with task id: f99d0206-aee4-4078-bb48-c60428680743/task-54\nTask Description: python3 -c '\nimport time, math, sys\nimport numpy as np\nsys.path.insert(0, \"/opt/fullsim\")\nfrom tools.fullsim.session import Session\n\ns = Session(\"soccer\", level=2, seed=7)\nimport soccer as S\n\ndef run_test():\n    s.reset()\n    last_sent_k = {\"A1\": -10, \"A2\": -10}\n\n    while not s.world.w.done:\n        m = s.world\n        w = m.w\n        if m.phase().state == \"unit_over\":\n            s.ready()\n            last_sent_k = {\"A1\": -10, \"A2\": -10}\n            continue\n\n        tr = w.truth(\"A\")\n        ball = np.asarray(tr[\"ball\"], float)\n        ball_v = np.asarray(tr[\"ball_v\"], float)\n        robots = tr[\"robots\"]\n\n        avail = [r for r in (\"A1\", \"A2\") if not robots[r][\"bench\"] and not robots[r][\"fallen\"]]\n        if not avail:\n            s.step(1)\n            continue\n\n        def rob_cost(r):\n            p = np.asarray(robots[r][\"xy\"], float)\n            d = float(np.hypot(*(p - ball)))\n            if p[0] > ball[0] + 0.15:\n                d += 0.5\n            return d\n\n        avail_sorted = sorted(avail, key=rob_cost)\n        striker = avail_sorted[0]\n\n        out = []\n        for r in avail:\n            cur = w.prog[r]\n            if robots[r][\"kicking\"]:\n                kk = cur.get(\"kicker\")\n                if cur.get(\"style\") == \"step\" and kk is not None and kk.phase() == \"approach\":\n                    p = np.asarray(robots[r][\"xy\"], float)\n                \n… (7,151 characters left out) …\nse vmax)\n    # If within 1.15m of ball and an opponent is within 0.65m of us, wrap walk in step kick for _kick_exempt!\n    opp_dist_me = min([float(np.hypot(*(np.asarray(robots[o][\"xy\"]) - p))) for o in (\"B1\", \"B2\") if not robots[o][\"bench\"]] or [9.0])\n    if wcmd[\"type\"] == \"walk\" and float(np.hypot(*rel)) < 1.15 and opp_dist_me < 0.65 and abs(err_y) < 0.45:\n        kick_aim = round(float(np.clip(err_y, lo, hi)), 4)\n        cmd = dict(type=\"kick\", style=\"step\", power=0.0 if kickoff_touch else 1.0, aim=kick_aim, v=wcmd[\"v\"])\n        if foot_s != \"right\":\n            cmd[\"foot\"] = foot_s\n        return cmd\n    return wcmd\n\nt0 = time.time()\nscore, winner, reason, k, stats = run_test()\nprint(\"Final:\", score, \"winner:\", winner, \"reason:\", reason, \"k:\", k, \"wall:\", round(time.time()-t0, 1))\nfor r, st in stats.items():\n    print(r, {k: st[k] for k in (\"goals\", \"own_goals\", \"touches\", \"kicks\", \"kicks_cancelled\", \"falls\", \"penalties\", \"offenses\")})\n'\nTask logs are available at: file:///root/.gemini/antigravity-cli/brain/f99d0206-aee4-4078-bb48-c60428680743/.system_generated/tasks/task-54.log\nYOU MUST TAKE ONE OF THE FOLLOWING TWO ACTIONS: A) either proceed to other relevant work (if any) or, B) simply update the user with a short message (that you have launched the command and will wait for it to finish) and end the turn.\n DO NOTHING ELSE.\n When updating the user, always respond in the language of the user (defaulting to English). Never mix two or more languages in the same response.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":29,"t":1133.0,"say":"I am now testing the dual-striker controller with aimed step-kicks and kick-exempt approach near opponents. I will wait for the simulation to complete.","calls":[],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":31,"t":1172.0,"say":"","calls":[{"kind":"edit","label":"program.py","code":"/app/output/policy/program.py\n\nfrom __future__ import annotations\n\nimport math\nimport sys\nfrom pathlib import Path\n\nimport numpy as np\n\nfor _p in (\"/opt/dev\", \"/opt/fullsim\"):\n    if Path(_p).is_dir():\n        sys.path.insert(0, _p)\n        sys.path.insert(0, str(Path(_p) / \"sports\" / \"soccer\"))\n        break\n\nimport soccer as S  # noqa: E402\n\n\nclass Policy:\n    def __init__(self, config: dict):\n        cfg = (config.get(\"spec\") or {}).get(\"cfg\") or {}\n        self.seed = int(cfg.get(\"seed\", 7))\n        self.last_sent_k = {\"A1\": -10, \"A2\": -10}\n        self.kick_k0 = {\"A1\": -10, \"A2\": -10}\n\n    def reset_unit(self):\n        self.last_sent_k = {\"A1\": -10, \"A2\": -10}\n        self.kick_k0 = {\"A1\": -10, \"A2\": -10}\n\n    def act(self, view: dict):\n        state = view.get(\"state\") or {}\n        phase = state.get(\"phase\")\n        if phase == \"unit_over\":\n            self.reset_unit()\n            return {\"type\": \"ready\"}\n        if phase != \"playing\":\n            return None\n        truth = view.get(\"truth\")\n        if not truth:\n            return None\n\n        k = int(truth[\"k\"])\n        ball = np.asarray(truth[\"ball\"][\"pos\"][:2], float)\n        ball_v = np.asarray(truth[\"ball\"][\"vel\"][:2], float)\n        robots = truth[\"robots\"]\n        ref = truth[\"referee\"]\n        r_state = state.get(\"robots\") or {}\n\n        avail = [r for r in (\"A1\", \"A2\") if not robots[r][\"bench\"] and not robots[r][\"fallen\"]]\n        if not avail:\n            return None\n\n        def rob_cost(r: str) -> float:\n            p = np.asarray(robots[r][\"xy\"], float)\n            d = float(np.hypot(*(p - ball)))\n            if p[0] > ball[0] + 0.15:\n                d += 0.5\n            return d\n\n        avail_sorted = sorted(avail, key=rob_cost)\n        striker = avail_sorted[0]\n\n        ko = ref.get(\"kickoff\")\n        sho = ref.get(\"shootout\")\n        ref_state = ref.get(\"state\")\n\n        out = []\n        for r in avail:\n            cur = (r_state.get(r[1]) or {}).get(\"command\") or {}\n            if robots[r][\"kicking\"] or cur.get(\"type\") == \"kick\":\n                if cur.get(\"style\") == \"step\" and cur.get(\"phase\") == \"approach\":\n                    p = np.asarray(robots[r][\"xy\"], float)\n                    yaw = float(robots[r][\"yaw\"])\n                    rel = p - ball\n                    u = np.array([math.cos(yaw), math.sin(yaw)])\n                    behind = float(np.dot(rel, u))\n                    lat = float(np.dot(rel, np.array([-u[1], u[0]])))\n                    if behind > -0.10 or behind < -1.10 or abs(lat) > 0.30 or (k - self.kick_k0[r]) >= 12:\n                        pass\n                    else:\n                        continue\n                else:\n                    continue\n\n            role = \"striker\"\n            if r != striker:\n                p_s = np.asarray(robots[striker][\"xy\"], float)\n                if S.in_own_area(p_s, 0.35) or S.in_own_area(ball, 0.2):\n                    role = \"support\"\n\n            if ko and not ko[\"in_play\"]:\n                if ko[\"\n… (4,829 characters left out) …\n  c = np.array([S.HALF_L, y])\n                v = c - ball\n                n = float(np.hypot(*v))\n                d = min(\n                    [\n                        abs(float(v[0] * (o[1] - ball[1]) - v[1] * (o[0] - ball[0])) / n)\n                        for o in opps\n                        if 0 < float(np.dot(o - ball, v)) < n * n\n                    ]\n                    or [9.0]\n                )\n                if d > gap:\n                    best, gap = c, d\n            aim = best\n\n        u = aim - ball\n        u /= max(np.hypot(*u), 1e-6)\n        left = np.array([-u[1], u[0]])\n        rel = p - ball\n        behind = float(np.dot(rel, u))\n        lat = float(np.dot(rel, left))\n        face = math.atan2(u[1], u[0])\n        err_y = float(S._wrap(face - yaw))\n\n        kickoff_touch = bool(\n            ko and ko[\"team\"] == \"A\" and not ko[\"touched_outside\"] and float(np.hypot(*ball)) < S.CENTER_R\n        )\n\n        foot_s, ry = (\"right\", S.STEP[\"line_m\"]) if lat >= 0 else (\"left\", -S.STEP[\"line_m\"])\n        lo, hi = (\n            (-S.STEP[\"aim_max\"], S.STEP[\"aim_in_max\"])\n            if foot_s == \"right\"\n            else (-S.STEP[\"aim_in_max\"], S.STEP[\"aim_max\"])\n        )\n        Ps = b_pred - 0.62 * u + ry * left\n\n        if (\n            -0.95 < behind < -0.22\n            and abs(lat - ry) < 0.075\n            and (lo - 0.05) <= err_y <= (hi + 0.05)\n            and abs(err_y) < 0.25\n        ):\n            power = 0.0 if kickoff_touch else 1.0\n            c, s_ = math.cos(yaw), math.sin(yaw)\n            e_lat = ry - lat\n            vy = float(np.clip(1.5 * e_lat * float(-s_ * left[0] + c * left[1]), -vlat, vlat))\n            kick_aim = round(float(np.clip(err_y, lo, hi)), 4)\n            cmd = dict(\n                type=\"kick\",\n                style=\"step\",\n                power=power,\n                aim=kick_aim,\n                v=[round(0.60, 3), round(vy, 3), round(float(np.clip(1.8 * err_y, -wmax, wmax)), 3)],\n            )\n            if foot_s != \"right\":\n                cmd[\"foot\"] = foot_s\n            return cmd\n\n        if behind > -0.18:\n            side = 1.0 if lat >= 0 else -1.0\n            tgt = b_pred - 0.70 * u + side * 0.68 * left\n            return go(tgt, face=face if np.hypot(*(p - tgt)) < 0.8 else None)\n\n        wcmd = go(Ps, face=face, vm=0.38 if np.hypot(*(Ps - p)) < 0.26 else vmax)\n        opp_dist_me = min(\n            [float(np.hypot(*(np.asarray(robots[o][\"xy\"]) - p))) for o in (\"B1\", \"B2\") if not robots[o][\"bench\"]]\n            or [9.0]\n        )\n        if wcmd[\"type\"] == \"walk\" and float(np.hypot(*rel)) < 1.15 and opp_dist_me < 0.65 and abs(err_y) < 0.45:\n            kick_aim = round(float(np.clip(err_y, lo, hi)), 4)\n            cmd = dict(type=\"kick\", style=\"step\", power=0.0 if kickoff_touch else 1.0, aim=kick_aim, v=wcmd[\"v\"])\n            if foot_s != \"right\":\n                cmd[\"foot\"] = foot_s\n            return cmd\n        return wcmd\n\n\ndef make_policy(config: dict) -> Policy:\n    return Policy(config)\n"}],"out":{"text":"Created file file:///app/output/policy/program.py with requested content.\nIf relevant, proactively run terminal commands to execute this code for the USER. Don't ask for permission.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":32,"t":1222.0,"say":"","calls":[{"kind":"robot","label":"Verifying program.py in Session","code":"python3 -c '\nimport time, sys\nsys.path.insert(0, \"/opt/fullsim\")\nsys.path.insert(0, \"/app/output/policy\")\nfrom tools.fullsim.session import Session\nimport program\n\ns = Session(\"soccer\", level=2, seed=7)\npol = program.make_policy({\"spec\": s.spec()})\n\nt0 = time.time()\nrefused = 0\nwhile not s.world.w.done:\n    m = s.world\n    if m.phase().state == \"unit_over\":\n        view = {\"state\": {\"phase\": \"unit_over\"}, \"truth\": None, \"scores\": m.scores()}\n        cmd = pol.act(view)\n        assert cmd == {\"type\": \"ready\"}\n        s.ready()\n        continue\n    view = {\n        \"state\": {\"step\": m.k, \"phase\": \"playing\", **m.observe(\"A\")},\n        \"truth\": s.ad.truth(m),\n        \"scores\": m.scores(),\n    }\n    ans = pol.act(view)\n    if ans is None:\n        s.step(1)\n    else:\n        cmds = ans if isinstance(ans, list) else [ans]\n        for c in cmds:\n            try:\n                s.act(c)\n            except Exception as e:\n                refused += 1\n                print(\"Refused:\", e)\n        s.step(2)\n\nprint(\"Final:\", s.world.w.score, \"winner:\", s.world.w.winner, \"reason:\", s.world.w.end_reason, \"k:\", s.world.w.k, \"refused:\", refused, \"wall:\", round(time.time()-t0, 1))\n'"}],"out":{"text":"Tool is running as a background task with task id: f99d0206-aee4-4078-bb48-c60428680743/task-60\nTask Description: python3 -c '\nimport time, sys\nsys.path.insert(0, \"/opt/fullsim\")\nsys.path.insert(0, \"/app/output/policy\")\nfrom tools.fullsim.session import Session\nimport program\n\ns = Session(\"soccer\", level=2, seed=7)\npol = program.make_policy({\"spec\": s.spec()})\n\nt0 = time.time()\nrefused = 0\nwhile not s.world.w.done:\n    m = s.world\n    if m.phase().state == \"unit_over\":\n        view = {\"state\": {\"phase\": \"unit_over\"}, \"truth\": None, \"scores\": m.scores()}\n        cmd = pol.act(view)\n        assert cmd == {\"type\": \"ready\"}\n        s.ready()\n        continue\n    view = {\n        \"state\": {\"step\": m.k, \"phase\": \"playing\", **m.observe(\"A\")},\n        \"truth\": s.ad.truth(m),\n        \"scores\": m.scores(),\n    }\n    ans = pol.act(view)\n    if ans is None:\n        s.step(1)\n    else:\n        cmds = ans if isinstance(ans, list) else [ans]\n        for c in cmds:\n            try:\n                s.act(c)\n            except Exception as e:\n                refused += 1\n                print(\"Refused:\", e)\n        s.step(2)\n\nprint(\"Final:\", s.world.w.score, \"winner:\", s.world.w.winner, \"reason:\", s.world.w.end_reason, \"k:\", s.world.w.k, \"refused:\", refused, \"wall:\", round(time.time()-t0, 1))\n'\nTask logs are available at: file:///root/.gemini/antigravity-cli/brain/f99d0206-aee4-4078-bb48-c60428680743/.system_generated/tasks/task-60.log\nYOU MUST TAKE ONE OF THE FOLLOWING TWO ACTIONS: A) either proceed to other relevant work (if any) or, B) simply update the user with a short message (that you have launched the command and will wait for it to finish) and end the turn.\n DO NOTHING ELSE.\n When updating the user, always respond in the language of the user (defaulting to English). Never mix two or more languages in the same response.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":33,"t":1247.0,"say":"I have updated [`program.py`](file:///app/output/policy/program.py) with the 10–0 mercy-winning controller and am now verifying it in `Session` using only the `view` dictionary. I will wait for the verification run to complete.","calls":[],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":35,"t":1287.0,"say":"","calls":[{"kind":"robot","label":"Testing policy robustness across step modes","code":"python3 -c '\nimport time, sys, random\nsys.path.insert(0, \"/opt/fullsim\")\nsys.path.insert(0, \"/app/output/policy\")\nfrom tools.fullsim.session import Session\nimport program\n\ns = Session(\"soccer\", level=2, seed=7)\n\nfor mode in (\"step1\", \"jitter\"):\n    s.reset()\n    pol = program.make_policy({\"spec\": s.spec()})\n    rng = random.Random(42)\n    t0 = time.time()\n    refused = 0\n    while not s.world.w.done:\n        m = s.world\n        if m.phase().state == \"unit_over\":\n            view = {\"state\": {\"phase\": \"unit_over\"}, \"truth\": None, \"scores\": m.scores()}\n            pol.act(view)\n            s.ready()\n            continue\n        view = {\n            \"state\": {\"step\": m.k, \"phase\": \"playing\", **m.observe(\"A\")},\n            \"truth\": s.ad.truth(m),\n            \"scores\": m.scores(),\n        }\n        ans = pol.act(view)\n        if ans is None:\n            s.step(1)\n        else:\n            cmds = ans if isinstance(ans, list) else [ans]\n            for c in cmds:\n                try:\n                    s.act(c)\n                except Exception as e:\n                    refused += 1\n            s.step(1 if mode == \"step1\" else (2 if rng.random() < 0.85 else 1))\n    print(mode, \"Final:\", s.world.w.score, \"winner:\", s.world.w.winner, \"reason:\", s.world.w.end_reason, \"k:\", s.world.w.k, \"refused:\", refused, \"wall:\", round(time.time()-t0, 1))\n'"}],"out":{"text":"Tool is running as a background task with task id: f99d0206-aee4-4078-bb48-c60428680743/task-64\nTask Description: python3 -c '\nimport time, sys, random\nsys.path.insert(0, \"/opt/fullsim\")\nsys.path.insert(0, \"/app/output/policy\")\nfrom tools.fullsim.session import Session\nimport program\n\ns = Session(\"soccer\", level=2, seed=7)\n\nfor mode in (\"step1\", \"jitter\"):\n    s.reset()\n    pol = program.make_policy({\"spec\": s.spec()})\n    rng = random.Random(42)\n    t0 = time.time()\n    refused = 0\n    while not s.world.w.done:\n        m = s.world\n        if m.phase().state == \"unit_over\":\n            view = {\"state\": {\"phase\": \"unit_over\"}, \"truth\": None, \"scores\": m.scores()}\n            pol.act(view)\n            s.ready()\n            continue\n        view = {\n            \"state\": {\"step\": m.k, \"phase\": \"playing\", **m.observe(\"A\")},\n            \"truth\": s.ad.truth(m),\n            \"scores\": m.scores(),\n        }\n        ans = pol.act(view)\n        if ans is None:\n            s.step(1)\n        else:\n            cmds = ans if isinstance(ans, list) else [ans]\n            for c in cmds:\n                try:\n                    s.act(c)\n                except Exception as e:\n                    refused += 1\n            s.step(1 if mode == \"step1\" else (2 if rng.random() < 0.85 else 1))\n    print(mode, \"Final:\", s.world.w.score, \"winner:\", s.world.w.winner, \"reason:\", s.world.w.end_reason, \"k:\", s.world.w.k, \"refused:\", refused, \"wall:\", round(time.time()-t0, 1))\n'\nTask logs are available at: file:///root/.gemini/antigravity-cli/brain/f99d0206-aee4-4078-bb48-c60428680743/.system_generated/tasks/task-64.log\nYOU MUST TAKE ONE OF THE FOLLOWING TWO ACTIONS: A) either proceed to other relevant work (if any) or, B) simply update the user with a short message (that you have launched the command and will wait for it to finish) and end the turn.\n DO NOTHING ELSE.\n When updating the user, always respond in the language of the user (defaulting to English). Never mix two or more languages in the same response.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":36,"t":1310.0,"say":"I am now testing [`program.py`](file:///app/output/policy/program.py) across multiple step-timing modes (`step1` and randomized step jitter) to ensure robustness before running the formal self-test. I will wait for this test to complete.","calls":[],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":38,"t":1465.0,"say":"","calls":[{"kind":"shell","label":"Running formal self-test of program.py","code":"cat /run/rcb-limits/usage.json && PYTHONPATH=/opt/fullsim python3 -m tools.fullsim.formal --sport soccer --policy /app/output/policy --level 2 --seed 7 --out /tmp/my-self-test-1 --unsafe-local"}],"out":{"text":"Tool is running as a background task with task id: f99d0206-aee4-4078-bb48-c60428680743/task-68\nTask Description: cat /run/rcb-limits/usage.json && PYTHONPATH=/opt/fullsim python3 -m tools.fullsim.formal --sport soccer --policy /app/output/policy --level 2 --seed 7 --out /tmp/my-self-test-1 --unsafe-local\nTask logs are available at: file:///root/.gemini/antigravity-cli/brain/f99d0206-aee4-4078-bb48-c60428680743/.system_generated/tasks/task-68.log\nYOU MUST TAKE ONE OF THE FOLLOWING TWO ACTIONS: A) either proceed to other relevant work (if any) or, B) simply update the user with a short message (that you have launched the command and will wait for it to finish) and end the turn.\n DO NOTHING ELSE.\n When updating the user, always respond in the language of the user (defaulting to English). Never mix two or more languages in the same response.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":39,"t":1481.0,"say":"I have launched the official `tools.fullsim.formal` self-test of [`program.py`](file:///app/output/policy/program.py) against the level-2 opponent on seed 7 at the formal `S = 5` realtime slowdown and will wait for it to finish.","calls":[],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":41,"t":2092.0,"say":"","calls":[{"kind":"shell","label":"Checking progress of formal self-test","code":"cat /tmp/my-self-test-1/match/final.json 2>/dev/null | head -n 40; tail -n 5 /tmp/my-self-test-1/decisions.jsonl 2>/dev/null; cat /run/rcb-limits/usage.json"}],"out":{"text":"The command exited with code 0.\nOutput:\n<truncated 1 lines>\n{\"seen\": 6141, \"command\": {\"type\": \"walk\", \"v\": [0.948, 0.067, 0.141], \"entity\": \"1\"}, \"code\": 200, \"reply\": {\"call\": \"act\", \"took_effect\": {\"step\": 6141, \"t_sim\": 122.82000000000001, \"wall_s_after_request\": 0.09217071393504739, \"robot\": 1}, \"now_step\": 6142}}\r\n{\"seen\": 6143, \"command\": {\"type\": \"walk\", \"v\": [0.948, 0.055, 0.092], \"entity\": \"2\"}, \"code\": 200, \"reply\": {\"call\": \"act\", \"took_effect\": {\"step\": 6143, \"t_sim\": 122.86, \"wall_s_after_request\": 0.09299196605570614, \"robot\": 2}, \"now_step\": 6144}}\r\n{\"seen\": 6145, \"command\": {\"type\": \"walk\", \"v\": [0.949, 0.05, 0.105], \"entity\": \"1\"}, \"code\": 200, \"reply\": {\"call\": \"act\", \"took_effect\": {\"step\": 6145, \"t_sim\": 122.9, \"wall_s_after_request\": 0.09378487290814519, \"robot\": 1}, \"now_step\": 6146}}\r\n{\"seen\": 6147, \"command\": {\"type\": \"walk\", \"v\": [0.947, 0.071, 0.12], \"entity\": \"2\"}, \"code\": 200, \"reply\": {\"call\": \"act\", \"took_effect\": {\"step\": 6147, \"t_sim\": 122.94, \"wall_s_after_request\": 0.09241707297042012, \"robot\": 2}, \"now_step\": 6148}}\r\n{\r\n \"t\": 1792429430.93,\r\n \"measured\": true,\r\n \"gpu_memory_gb\": 0.0,\r\n \"memory_gb\": 0.703,\r\n \"processes\": 7,\r\n \"disk_gb\": 0.222,\r\n \"enforced\": 0,\r\n \"limits\": {\r\n  \"max_gpu_memory_gb\": 0.0,\r\n  \"max_memory_gb\": 13.0,\r\n  \"max_processes\": 512,\r\n  \"max_disk_gb\": 20.0,\r\n  \"sustain_sec\": 60.0,\r\n  \"window_sec\": 120.0\r\n },\r\n \"violations\": [],\r\n \"emergency_kills\": 0,\r\n \"last_emergency_kill\": null,\r\n \"elapsed_s\": 2115.1,\r\n \"time_limit_s\": 7200,\r\n \"time_left_s\": 5084.9,\r\n \"deadline_utc\": \"2026-10-19T18:28:35Z\"\r\n}","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":42,"t":2099.0,"say":"","calls":[{"kind":"shell","label":"Checking score in formal self-test","code":"python3 -c 'import json; d=json.load(open(\"/tmp/my-self-test-1/match/final.json\")); print(\"steps:\", d.get(\"steps\"), \"scores:\", d.get(\"scores\"), \"phase:\", d.get(\"phase\"))'"}],"out":{"text":"The command exited with code 0.\nOutput:\nsteps: 6350 scores: {'A': 4, 'opponent': 0} phase: running","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":43,"t":2118.0,"say":"The formal self-test (`task-68`) is progressing smoothly: at step 6,350 (127 seconds of game time), our policy is leading `4:0` (`{'A': 4, 'opponent': 0}`). I will continue waiting for the match and replay verification to finish.","calls":[],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":45,"t":2705.0,"say":"","calls":[{"kind":"shell","label":"Cleaning up temp files and checking output directory","code":"rm -rf /tmp/my-self-test-1 && ls -la /app/output/policy && cat /run/rcb-limits/usage.json"}],"out":{"text":"The command exited with code 0.\nOutput:\ntotal 20\r\ndrwxr-xr-x 2 root root  4096 Oct 19 16:32 .\r\ndrwxr-xr-x 1 root root  4096 Oct 19 16:32 ..\r\n-rw-r--r-- 1 root root 10798 Oct 19 16:49 program.py\r\n{\r\n \"t\": 1792430055.95,\r\n \"measured\": true,\r\n \"gpu_memory_gb\": 0.0,\r\n \"memory_gb\": 0.143,\r\n \"processes\": 5,\r\n \"disk_gb\": 0.224,\r\n \"enforced\": 0,\r\n \"limits\": {\r\n  \"max_gpu_memory_gb\": 0.0,\r\n  \"max_memory_gb\": 13.0,\r\n  \"max_processes\": 512,\r\n  \"max_disk_gb\": 20.0,\r\n  \"sustain_sec\": 60.0,\r\n  \"window_sec\": 120.0\r\n },\r\n \"violations\": [],\r\n \"emergency_kills\": 0,\r\n \"last_emergency_kill\": null,\r\n \"elapsed_s\": 2740.1,\r\n \"time_limit_s\": 7200,\r\n \"time_left_s\": 4459.9,\r\n \"deadline_utc\": \"2026-10-19T18:28:35Z\"\r\n}","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":46,"t":2726.0,"say":"I have developed and verified the soccer policy in [`program.py`](file:///app/output/policy/program.py) (`make_policy(config)` / [`Policy.act`](file:///app/output/policy/program.py#L31-L115)).\n\n### Key Design & Improvements\n- **Dual-Striker Coordination & Penalty Prevention**: Both robots (`A1` and `A2`) dynamically contest the ball based on distance and behind-ball positioning, while automatically holding the secondary robot outside our penalty area whenever the primary robot or ball is inside our penalty area (preventing illegal-defence penalties) and using radial + tangential repulsion around opponents and teammates.\n- **Aimed In-Walk Kicks (`style=\"step\"`)**: Uses both left and right feet with explicit `aim` adjustments toward the most open goal target (`y in {+0.85, 0.0, -0.85}`), widened behind-ball trigger bounds (`-0.95 < behind < -0.22`, allowing immediate kickoff touches without backing up), and approach-phase cancellation if the ball moves out of the strike cone.\n- **Kick-Exempt Close Approach & Rate Filtering**: Wraps close-range contested approaches in `style=\"step\"` kicks (exempting the robot from pushing penalties within 1.3 m of the ball under HSL Law 12) and filters redundant `walk`/`stand` commands so `act(view)` never stalls or triggers command refusals.\n\n### Verification\n- **In-Process `Session` Tests**: Won `10:0` by the 10-goal mercy rule across `step2` (`k = 11099`), `step1` (`k = 10049`), and randomized step-jitter (`k = 29122`) regimes with `0` refused commands.\n- **Official Formal Self-Test (`tools.fullsim.formal`, `level=2`, `seed=7`, `slowdown=5`)**:\n  - **Score**: `10:0` (`points_for: 10`, `points_against: 0`, mercy rule win at `steps: 11775`)\n  - **Verdict**: `success: 1`, `valid: 1`, `deterministic: 1`, `refused: 0`, `policy_error: 0`, `infra_error: 0`","calls":[],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}}]}

runs/embodied-olympics-match-soccer-match-i00-privileged--ufuxjhv/replay.mp4

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