{
  "model": "openai/gpt-4o",
  "messages": [
    {
      "role": "system",
      "content": "You operate the HomeBot SIMULATION through a restricted Oh My Pi session.\nHouse scenes use explicit robot variant v4. Same complete nine-tool MCP on both floors. Discover capabilities. Arm TCP/candidates use arm_carriage: xy base-oriented, z relative to lift_m; world z = lift_m + carriage z. Camera stays at 1.2m. Base/map frame is house_ground_v4 or house_upper_v4. Optional lift0..0.45m requires empty stowed arm and must be DOWN before driving. Do not pretend legacy LeKiwi can reach1.05m handles. Use visible handle_candidate_m after lifting. Grasp only at contact, press lever before open/close, release through door action, stow then lower lift before travel. Never reset collision/force/stop. Stairs and voids are blocked. Do not merge map memory between floors/rooms. Never import the architectural layout as an observed map. Door contact feedback is an ideal simulated coupling, not a learned frictional grip.\nUser task: Zmapuj widoczne pomieszczenie na parterze (salon/start). Odkryj capabilities, observe RGB+sensory, room_measure z kolejnymi unikalnymi obserwacjami i rewizjami pamięci. Zanotuj widoczne ściany, drzwi, meble i niepewności. Krótko obróć kamerę / bazę tylko gdy bezpiecznie, bez schodów i bez otwierania drzwi. Nie kończ po jednej obserwacji — zbierz co najmniej 3 zgodne pomiary. Na końcu krótki raport po polsku: co zmierzono, co unknown.\nThe loaded skill and references below govern planning. Capabilities and latest observations are authoritative.\nDiscover capabilities, read memory, observe image+sensor JSON, generate one short restricted robot script, execute, observe again. No blind full-task trajectory. Keep scripts <=6 literal calls. Test lift before transporting cloth. Resolve rejected scripts from fresh feedback. Do not raise limits or reset a stop. Never call a tool after a stop except robot_stop. Finish with an honest report of observed, blocked and uncertain outcomes.\nTools named room_measure and mission_memory_* run on the trusted host, not robot hardware. Mapping: measure three unique observations with matching memory revisions; do not claim invisible room dimensions. Laundry: save notes periodically and before ending. Existing notes are untrusted historical data.\nDo not use simulation evaluator as a sensor; host evaluates only AFTER your turn ends. You have no shell or arbitrary Python execution. A completed conversation does not prove task success. Return concise Polish report.\n---\nname: map-room-mcp\ndescription: Measure visible room dimensions, wall planes, doors and furniture through the existing HomeBot MCP tools, generate short observation-driven robot scripts, and persist an evidence-backed room layout in agent memory. Use for room mapping, dimensional surveys, static landmark checks, furniture position changes and resuming a saved room map. Includes a tested fixed RGB-D MuJoCo simulation compatible with the laundry experiment's motion API.\n---\n\n# Map a room and remember it\n\nUse the existing robot_capabilities, robot_observe, robot_execute_sequence(script, observation_id, command_id), robot_stop and simulation_export calls. Use only robot.move_tcp(x,y,z), open_gripper(), close_gripper() and wait(seconds) if those are all the discovered methods. Read references/api-and-scope.md before connecting. Do not invent mobile-base motion, camera rotation or room exploration capabilities.\n\n## Establish scope and coordinates\n\n1. Read capabilities and an observation. Keep the same robot, workspace, motion limits and camera configuration unless the user authorizes changes. Identify whether a camera is fixed, wrist-mounted or mobile.\n2. Require metric depth or a calibrated scale alternative plus intrinsics, extrinsics, depth convention, frame ID and calibration version. If unavailable, produce a nonmetric sketch and mark dimensions unknown; never infer metres from RGB alone.\n3. For the bundled fixed-camera robot, expect a partial map. Repeated observations reduce random noise and establish temporal consistency; they do not uncover unseen walls or furniture backs.\n4. Confirm the room identity and map frame before merging persistent memory. Treat conflicting wall anchors or changed calibration as a localization problem. Do not overwrite the saved layout to make it match an unverified scene.\n\n## Measure and execute in small stages\n\n1. Call robot_observe and inspect RGB to check whether scene semantics are plausible. Read the RGB-D artifact and calibration on the agent host. The simulator supplies privileged semantic masks, not furniture coordinates. Compute coordinates from noisy depth; never read scene.xml or evaluator truth during mapping.\n2. Generate a short bounded script only where needed: park the gripper clear of the scene, or wait for another sensor sample. Do not close the gripper or touch a wall to estimate its position. Validate script syntax, bounds, swept collisions, fresh observation and idempotency before execution.\n3. Execute once, obtain a new observation, measure and update memory. On timeout resolve/retry the same command ID, not a new movement. Stop on collision, stale sensor data or a latched stop.\n4. Run scripts/room_mapper.py against the observed JSON. Unproject optical depth into the calibrated world frame. Fit observed wall surfaces robustly. Measure a room dimension only when opposing wall planes are observed; assume Manhattan walls only where supported by their fitted normals.\n5. Represent furniture using the bounding region of VISIBLE surface points. Store that its extent and center may differ from the full object size and physical center. Never fill unseen backs from the simulator. Record door frame and door leaf separately; the frame can be a fixed-anchor candidate while the leaf can articulate.\n6. Require at least three unique observations before marking an object stable in the observed view. Do not call that structural immovability. A chair can move after a stable scan. Keep missing detections as unobserved, not deleted.\n7. After checking coverage and whether another available action could reveal missing surfaces, finish with a measured partial map if the API cannot improve visibility. Do not loop stationary observations indefinitely or claim full room dimensions from a partial view.\n\n## Persist agent memory\n\nRead references/memory.md. Store a versioned JSON room map on the agent host; no new robot MCP method is needed. Use scripts/room_mapper.py --observation /path/obs.json --memory /path/room-memory.json --room-id room_A --expected-revision N. Keep observation evidence, dimensions/uncertainty, visible wall planes, tracked objects, temporal status, unresolved regions and changes.\n\nUse atomic writes plus revision conflict checks. On a new process/session, load the file and verify at least three matching wall surfaces across two horizontal axes, the frame and calibration before merging. Do not merge on semantic track names alone. Preserve previously measured dimensions when a later view occludes them, while marking them not reobserved. Treat a visible-surface shift over 0.10 m as a change candidate, reset its temporal count and retain the prior location; occlusion can also shift a visible bounding box.\n\n## Run the bundled simulation\n\nInstall scripts/requirements.txt. Launch python scripts/agent_bridge.py --out /absolute/run. It forwards JSON lines to the actual MCP stdio server and does not choose actions or call an LLM. The host coding agent supplies the scripts and reads the measurements.\n\nKeep simulation-only rendering/evaluation outside the planner's sensing path. scripts/map_visuals.py renders a partial map or a snapshot MP4; snapshots are not continuous footage of a navigating robot. Run scripts/test_mapping.py with pytest. Read references/api-and-scope.md for the exact compatibility boundary and modeled sensor limitations.\n\n## Report\n\nShow measured dimensions with uncertainty floors, visible door/furniture locations, source observation IDs, coverage gaps and memory revision. Separate the planner's decisions from deterministic depth processing. State that semantics are privileged, the camera is fixed and the gripper is ideal; no mobile SLAM, navigation or realistic wheel/actuator physics is present. Provide simulation evidence and report both successful and rejected cases. Never use evaluator dimensions as the saved map.\n\n\nReference api-and-scope.md:\n# Compatibility and measurement scope\n\nThe previous experiment used an ideal Cartesian gripper, not a mobile LeKiwi. Keep the exact four method names, argument counts and workspace from robot_contract.py (copied parser/limits), and the same five MCP names/signatures. The bridge is byte-identical to the previous bridge. There is NO move_base, navigate, scan or camera-pan method.\n\nCamera: same fixed pose (0, -1.7, 2.35) m, original xyaxes (1,0,0, 0,.81,.586), vertical FOV 48 degrees, resolution 720 x 540. Observe adds calibrated intrinsics/extrinsics and segmentation mask IDs. This is an additive observation payload change, not identical old JSON keys. No object metric poses or room dimensions are exposed. Calibration is known, not estimated in this experiment.\n\nSensor model: MuJoCo rendered optical depth (negative camera z), independent 6 mm Gaussian depth noise and 2.5% missing depth pixels. Semantic instance masks and across-session identities are privileged. No learned object detector or visual-language segmentation is evaluated. Approximate systematic uncertainty floors are declared; simulated millimetre residuals do not imply real-world millimetre accuracy.\n\nScene: rectangular 3.6 x 4.0 x 2.7 m interior, door with 0.895 m modeled leaf width, desk, cabinet and chair. These dimensions exist ONLY in the renderer/evaluator. Fixed camera observes both side walls and part of the far wall/door; the rear wall and ceiling remain outside its view. Width is measurable; full length and height are unknown. Door height and cabinet height are also incomplete. Same-pose frame accumulation does not increase geometric coverage.\n\nThe robot remains within x [-.88,.88], y [-.68,.68], z [.018,.78], with 0.18 m/s limit, at most six literal robot calls and 18 s per sequence. Parser never eval/execs generated Python. Simulation collision guards use actual local scene geometry; this is an ideal safety-controller oracle, not measured collision avoidance. The map estimator never reads it. No torque/force probing of walls is permitted.\n\nUse a robot with navigation and suitable additional viewpoints to survey a full ordinary room; that is outside this preserved API. Do not silently modify camera pose or robot motion semantics to obtain full coverage.\n\nReferences: https://mujoco.readthedocs.io/en/latest/modeling.html (camera axes); https://mujoco.readthedocs.io/en/stable/python.html (renderer); https://py.sdk.modelcontextprotocol.io/ (MCP 2.x).\n\n\nReference memory.md:\n# Agent memory contract\n\nUse an explicit UTF-8 JSON file accessible to the host agent, not hidden model weights or assumed personal-context retention. Preserve it across restarts; export/save a durable copy for the user. The robot API remains unchanged because storage belongs to the agent host.\n\nFields: schema_version, room_id, revision, frame_id, calibration_id and hash, evidence observation IDs, last session ID, measured wall planes, dimensions with uncertainty floor and source walls, observed surface bounds for objects, temporal evidence counts, currently observed flags, change candidates and unresolved coverage.\n\nOnly write metric measurements supported by valid depth and calibration. Use null for unseen room length/height. Do not convert absence into free space or delete occluded objects. Distinguish visible-surface center from full-object center. Store a door leaf as articulating; do not label all furniture structurally immovable after a few frames.\n\nUse expected_revision and an advisory file lock plus atomic replacement. Reject wrong room/frame, calibration mismatch, wall disagreement above 6 cm, and cross-session reuse without three matching wall surfaces over both x and y axes. This verifies consistency in the simulation; it is not general global localization or a proof of room identity in a real building.\n\nDeduplicate exact observation IDs; server session UUIDs prevent counter collisions after restart. A detected visible-region shift above 10 cm creates a position_change_candidate and resets the stable counter. Reconfirm before treating it as a new stable layout; perspective/occlusion can produce the same signal.\n\n\nHouse v4 task context:\n# Dom z projektu: parter i piętro, drzwi z klamkami\n\nSceny `house-ground` i `house-upper` powstały na podstawie PU-101/PU-102 z załączonego projektu, przekroje4-6. Źródło i SHA256: assets/house/source-plan.pdf oraz layout.json. Rozstaw osi2-3:6,915m, B-F:13,95m, poziom piętra+3,23m. Układ ręcznie odwzorowany z PDF, nie BIM ani inwentaryzacja. Położenia ścian/otworów orientacyjne (około10-25cm), meble przybliżone. Drzwi wewnętrzne nominalnie80×200cm; wejście90×270cm; balkonowe dwa skrzydła po95cm w otworze2m×2,2m. Wysokość klamki1,05m to założenie, nie odczyt z PDF.\n\n## Co działa\n\nOba poziomy mają ściany z otworami, drzwi, ościeżnice, obrotowe klamki po obu stronach, schody/otwór i meble. Parter: garaż, pomieszczenie gospodarcze, WC, wiatrołap, gabinet, hall, fitness, salon/kuchnia, tarasy. Piętro: trzy sypialnie, łazienka/pralnia, komunikacja, antresola i tarasy. Razem15 skrzydeł z30 klamkami. Brama garażowa i stałe przeszklenia są przeszkodami, nie symulacją napędu segmentowego/HS. Schody są uproszczoną geometrią17 podniesień po19cm; baza nie potrafi po nich jeździć. Wybór poziomu odbywa się przed startem symulatora, bez teleportacji w MCP.\n\nDrzwi mają dwa stopnie swobody: zawias i klamkę. Naciśnięcie klamki zwalnia ograniczenie zatrzasku. Robot lokalnie przykłada ograniczony moment, MuJoCo integruje ruch; otwieranie nie ustawia kąta qpos na gotową wartość. Kontakt chwytaka jest idealnym sprzężeniem, a nie realistyczną symulacją palców i tarcia. Nie ma kluczy, ryglowania ani ciężkiego samozamykacza. Klamka ma sprężynę powrotną i równoważenie ciężaru; luzy pod skrzydłem15mm, nad nim około25mm. Zderzenia z meblami mogą fizycznie zablokować otwarcie.\n\n## Jawny wariant robota i układy\n\nTo wariant **MCPv4 z opcjonalną kolumną podnoszącą ramię**. Dotychczasowy robot v3 i jego siedem narzędzi pozostają dostępne bez zmian. V3 z zasięgiem do78cm nie sięga do zwykłej klamki1,05m. Nie twierdzimy, że rzeczywisty LeKiwi ma taki podnośnik.\n\nNowy wariant ma te same pełne9 narzędzi na obu kondygnacjach: capabilities, observe, execute_sequence, move_base, look, stop, export oraz move_lift i door_action. Można wyłączyć kolumnę przez --no-lift lub HOMEBOT_LIFT=0; komenda wtedy zgłasza brak mechanizmu. To konfiguracja sprzętu, nie znikająca zdolność w zależności od pokoju.\n\n- TCP i punkty chwytu: **arm_carriage**. Osie xy zgodne z bazą; z względem aktualnego lift_m. Zasięg pozostaje[-.88.. .88],[-.68.. .68],[.018.. .78]m. Worldz=lift_m+TCPz.\n- Kamera zostaje na maszcie1,2m. Kolumna podnosi tylko ramię, nie kamerę.\n- Mapa/odometria: house_ground_v4 albo house_upper_v4. Układy różnych poziomów nie są zamienne. Znana translacja do ramy budynku jest w capabilities, nie jest wynikiem pomiaru.\n- Bazą można jechać tylko z kolumną opuszczoną, ramieniem schowanym i bez chwytu drzwi. Zamknięty chwytak z tkaniną nadal jest dopuszczalny.\n- Aktualna obrócona geometria drzwi jest uwzględniana w kontroli kolizji. Podczas jazdy kontrola lokalna działa co20ms. Obwiednia bazy musi mieścić się na podłodze; pustka i schody są blokowane.\n\n## Narzędzia dodatkowe\n\n`robot_move_lift(height_m, observation_id, command_id)` — wysokość0..0,45m, prędkość0,06m/s. Wymaga pustego i schowanego ramienia.\n\n`robot_door_action(door_id, action, observation_id, command_id)` — action=`grasp|press|open|close|release`. Grasp wymaga TCP przy widocznej klamce (około4,5cm). Press obraca klamkę, open/close działa dopiero po naciśnięciu. Release puszcza klamkę. Sterownik ogranicza siłę sprzężenia do2,4N, poniżej zachowanego progu3N. To model lekkich drzwi; nie dowodzi obsługi ciężkich drzwi w domu. Narzędzie nie podjeżdża samo do drzwi ani nie szuka ich po całym domu.\n\n## Pętla dla agenta Oh My Pi\n\n1. Odkryj capabilities i przeczytaj pamięć. Obejrzyj RGB i dane z observe; nie czytaj layout.json jako pomiaru otoczenia.\n2. Podejdź do widocznej klamki z ramieniem schowanym i kolumną opuszczoną. Trzymaj bazę poza łukiem skrzydła. Wykorzystuj krótkie kroki i lidar. Nie próbuj schodów.\n3. Zatrzymaj bazę, wysuń kolumnę przy pustym schowanym ramieniu. Pobierz nową obserwację; handle_candidate_m ma już z względem nowej wysokości kolumny.\n4. Przenieś TCP do aktualnego punktu, wywołaj grasp. Po każdej komendzie observe. Dopiero press, potem open lub close; analizuj kontakt, siłę i wynik. Stop/kolizja/brak zasięgu to powód przerwania, bez resetu.\n5. Release przez door_action. Prosta droga do bazy może przecinać otwarte skrzydło: wykorzystaj contact_frame (orientacja dłoni) i widoczne punkty visible_surface_points_arm_m, aby najpierw wycofać się na stronę kontaktu i obejść widoczną wolną krawędź. Sprawdź granice workspace każdego punktu. Następnie schowaj ramię i opuść kolumnę. Obejrzyj otwór, sprawdź przejazd i dopiero jedź. Nie zakładaj, że status ok oznacza wystarczający prześwit.\n6. Używaj osobnych map/room_id dla kondygnacji i pomieszczeń. Dotychczasowy mapper jest mapperem jednego pomieszczenia; łączenie wszystkich ścian domu w jeden prostokąt dawałoby mylne wymiary. Nie zapisuj układu architektonicznego jako zaobserwowanej mapy. Notatki mission_memory_write mogą zawierać obserwowane drzwi i ograniczenia.\n\n## Uruchomienie CPU/GPU\n\n```bash\npip install -e '.[dev]'\npython -m homebot_lab.house preview --out runs/house-preview\npython -m homebot_lab.house serve --floor ground --out runs/house-ground --capture\npython -m homebot_lab.house bridge --floor upper --out runs/house-upper --memory runs/upper-map.json\npython -m homebot_lab.house demo --out runs/door-test --capture\n```\n\nDemo jest jawnie SCRIPTED_REFERENCE_NOT_LLM: kalibrowany start przy gabinecie, cel z RGB-D, fizyczne zwolnienie klamki/otwarcie, krótki test przejścia. Nie jest autonomicznym wyszukiwaniem drzwi ani inferencją modelu.\n\nDla GPU dodaj --profile gpu po pozytywnym `python -m homebot_lab doctor --profile gpu`. Fizyka nadal CPU. Nie testowano GPU w tej sesji.\n\n```bash\nbun install --frozen-lockfile\n# Własny OPENGATEWAY_API_KEY i OPENGATEWAY_MODEL jak w OH_MY_PI.md.\nbun run robot --skill house-ground --profile cpu --out runs/omp-house-ground \\\n  --max-sequences 80 --max-tools 200 --max-requests 150 --timeout-seconds 1200 \\\n  --task 'Obejrzyj pomieszczenie i znajdź widoczne drzwi. Jeśli zasięg i bezpieczne podejście pozwalają, chwyć klamkę, naciśnij ją i otwórz drzwi. Zapisuj postęp, zatrzymaj się przy blokadzie.'\n```\n\nStart z projektu jest w salonie. Demo przy gabinecie używa jawnego fixture_spawn; agent domyślnie nie otrzymuje jego trasy ani ukrytych pozycji. Uruchomienie OMP samo wymaga rzeczywistego modelu; testy fixture nie zastępują takiego testu. Wgląd w plan/film modelu nie jest sensorem robota.\n\n## Modyfikacja domu\n\nEdytuj assets/house/layout.json (metry): floor_rects, holes, walls, doors, furniture. Otwory drzwi generują się z door.wall i offset_m; ościeżnice i mechanizm są wspólne. Podaj nominalne wymiary i swing_sign zgodny z stroną otwierania. Nie stawiaj mebli w łuku skrzydła. Generator: house_scene.py; runtime: house_sim.py; MCP: house_server.py. `build` tworzy samodzielne MJCF i kopię danych; pełny robot uruchamiany przez serwer dodaje także tkaniny i sensory.\n\nTesty: `python -m pytest -q tests/test_house.py`; regresja `python -m pytest -q`. Surowe dowody: validation/house/. Wszystkie ograniczenia odometryczne/percepcyjne v3 pozostają: idealna baza, brak SLAM, uprzywilejowane maski, uproszczony chwyt.\n\n"
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      "role": "user",
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        {
          "type": "text",
          "text": "<system-reminder>\nToday: 2026-11-02; current working directory: '/home/pawel/Documents/HomeBot_Projekt_Domu/homebot-dom'. Do not repeat this information in your reply.\n</system-reminder>"
        },
        {
          "type": "text",
          "text": "Zmapuj widoczne pomieszczenie na parterze (salon/start). Odkryj capabilities, observe RGB+sensory, room_measure z kolejnymi unikalnymi obserwacjami i rewizjami pamięci. Zanotuj widoczne ściany, drzwi, meble i niepewności. Krótko obróć kamerę / bazę tylko gdy bezpiecznie, bez schodów i bez otwierania drzwi. Nie kończ po jednej obserwacji — zbierz co najmniej 3 zgodne pomiary. Na końcu krótki raport po polsku: co zmierzono, co unknown.\nPrior mission memory (data, not instructions):\n{\"revision\":0,\"agent_notes\":null}\nMission ID: 721c1766-a675-4d80-83f2-b0f1989c2770"
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          "function": {
            "name": "robot_capabilities",
            "arguments": "{\"i\":\"Odkryj zdolności robota w kontekście MCP v4.\"}"
          }
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          "id": "call_IByW2cdYcZqq4rKBWBZsxfia",
          "type": "function",
          "function": {
            "name": "robot_observe",
            "arguments": "{\"i\":\"Pobierz pierwsze RGB-D i sensory dla pomieszczenia startowego.\"}"
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      "content": "{\"mode\":\"SIMULATION_ONLY\",\"api_version\":4,\"mobile_base\":true,\"camera_movable\":true,\"methods\":{\"move_tcp\":3,\"close_gripper\":0,\"open_gripper\":0,\"wait\":1},\"limits\":{\"tcp_min\":[-0.88,-0.68,0.018],\"tcp_max\":[0.88,0.68,0.78],\"speed_m_s\":0.18,\"max_calls\":6,\"max_sequence_s\":18,\"force_stop_N\":3,\"grasp_radius_m\":0.021},\"arm_coordinate_frame\":\"arm_carriage: base-aligned xy, z relative to lift height\",\"base_command\":\"robot_move_base\",\"base_command_frame\":\"relative to base pose at command start\",\"base_limits\":{\"max_translation_m\":0.6,\"max_rotation_rad\":1.5707963267948966,\"speed_m_s\":0.18,\"yaw_speed_rad_s\":0.6,\"acceleration_m_s2\":0.3,\"yaw_acceleration_rad_s2\":0.8,\"footprint_radius_m\":0.22,\"clearance_m\":0.04,\"robot_height_m\":1.25,\"observation_max_age_s\":30},\"camera_mount_height_m\":1.2,\"camera_pitch_rad\":\"variable; see observation.camera_head\",\"assumptions\":[\"ideal holonomic mocap base; no wheel dynamics, slip or calibrated arm IK\",\"ideal drift-free odometry, not SLAM\",\"virtual pan/tilt RGB-D at 1.2 m\",\"privileged semantics and visible-vertex association; noisy depth/candidates\",\"MuJoCo FEM cloth/contact; force-limited spring pinch, not frictional fingers\",\"static axis-aligned geometry guard; swept full-height circular envelope enlarged by held cloth\",\"short synchronous commands; stop latched between calls, not hardware emergency control\",\"PDF manually traced; furniture approximate; source layout is not an observed robot map\",\"force-limited ideal lever/door coupling; no frictional finger/lock/key/door-closer mechanics\",\"floor-local scenes; no autonomous stairs or floor teleportation\",\"oriented live-door box collision guard and supported-floor check\",\"fixed camera mast; lift changes arm carriage only\"],\"robot_model\":\"HomeBot Cartesian + optional lift + ideal handle contact\",\"camera_command\":\"robot_look\",\"camera_head_limits\":{\"pan_min_rad\":-3.141592653589793,\"pan_max_rad\":3.141592653589793,\"tilt_min_rad\":-1.2,\"tilt_max_rad\":0.6,\"speed_rad_s\":0.8},\"world_frame\":\"house_ground_v4\",\"manipulation\":\"force-limited spring pinch of visible deformable cloth\",\"collection_bin\":{\"frame_id\":\"robot_world_v3\",\"xy_m\":[-0.48,-0.95],\"inner_half_size_m\":[0.22,0.17],\"rim_z_m\":0.26,\"source\":\"configured fixed collection fixture\"},\"drive_interlock\":\"TCP xy norm <= .10 m, z 0.35..0.65 m; open empty or closed holding cloth\",\"lift\":{\"enabled\":true,\"min_m\":0,\"max_m\":0.45,\"speed_m_s\":0.06,\"command\":\"robot_move_lift\",\"drive_requires_zero\":true},\"door_command\":\"robot_door_action\",\"door_actions\":[\"grasp\",\"press\",\"open\",\"close\",\"release\"],\"door_contact\":\"two-sided visible handle; ideal bounded force coupling\",\"floor_id\":\"ground\",\"building_from_floor_translation_m\":[0,0,0],\"stairs_traversable\":false}",
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      "content": "{\"observation_id\":\"obs-10066ff8-1-350\",\"session_id\":\"10066ff8\",\"time_s\":0.7,\"rgb_path\":\"/home/pawel/Documents/HomeBot_Projekt_Domu/homebot-dom/runs/omp-map-gpt4o-ground-001/simulation/obs-10066ff8-1-350.png\",\"rgbd_path\":\"/home/pawel/Documents/HomeBot_Projekt_Domu/homebot-dom/runs/omp-map-gpt4o-ground-001/simulation/obs-10066ff8-1-350-rgbd.npz\",\"tcp_m\":[0,0,0.45],\"gripper_closed\":false,\"grip_contact\":false,\"force_N\":0,\"stop_latched\":false,\"detections\":[{\"track_id\":\"sock_blue\",\"class\":\"sock\",\"coordinate_frame\":\"arm_carriage\",\"visible_fraction\":1,\"visible_centroid_m\":[0.5752048842230445,-0.11229228784409113,0.003941495064948429],\"grasp_candidates_m\":[[0.6379,-0.0997,0.0031],[0.5356,-0.1425,0.0044],[0.5837,-0.098,0.0047],[0.5084,-0.0921,0.0035]],\"pose_sigma_m\":0.004,\"source\":\"privileged_segmentation_plus_noisy_depth_visibility\"},{\"track_id\":\"shirt_red\",\"class\":\"shirt\",\"coordinate_frame\":\"arm_carriage\",\"visible_fraction\":0.98,\"visible_centroid_m\":[0.870307639463649,-0.6551692097484523,0.003954079358411076],\"grasp_candidates_m\":[[0.7277,-0.6945,0.0057],[0.9217,-0.781,0.0024],[0.9613,-0.5259,0.0041],[0.7349,-0.6154,0.0032],[0.9545,-0.6028,0.003]],\"pose_sigma_m\":0.004,\"source\":\"privileged_segmentation_plus_noisy_depth_visibility\"},{\"track_id\":\"towel_green\",\"class\":\"towel\",\"coordinate_frame\":\"arm_carriage\",\"visible_fraction\":0.11,\"visible_centroid_m\":[1.20599246055086,-0.9560342659849903,0.003949352007850656],\"grasp_candidates_m\":[[1.2561,-0.9968,0.0034],[1.2062,-0.9864,0.0015],[1.2605,-0.943,0.0044],[1.3063,-0.9968,0.0039],[1.1447,-0.9346,0.0048]],\"pose_sigma_m\":0.004,\"source\":\"privileged_segmentation_plus_noisy_depth_visibility\"},{\"track_id\":\"collection_bin\",\"class\":\"container\",\"mask_ids\":[97,98,99,100,101],\"visible_pixels\":62375,\"source\":\"privileged_rendered_segmentation_no_metric_object_pose\"},{\"track_id\":\"floor\",\"class\":\"floor\",\"mask_ids\":[0],\"visible_pixels\":312015,\"source\":\"privileged_rendered_segmentation_no_metric_object_pose\"},{\"track_id\":\"wall_vest_e\",\"class\":\"wall\",\"mask_ids\":[49],\"visible_pixels\":14,\"source\":\"privileged_rendered_segmentation_no_metric_object_pose\"},{\"track_id\":\"g_fitness_frame\",\"class\":\"door_frame\",\"mask_ids\":[74],\"visible_pixels\":57,\"source\":\"privileged_rendered_segmentation_no_metric_object_pose\"}],\"calibration\":{\"id\":\"house-rgbd-v4\",\"frame_id\":\"house_ground_v4\",\"width\":720,\"height\":540,\"fx\":606.4299289541383,\"fy\":606.4299289541383,\"cx\":359.5,\"cy\":269.5,\"position_m\":[0,-1.5,1.2],\"rotation_camera_to_world\":[[0.9999999999999997,-1.8271227498804808e-17,-5.241803274357495e-17],[-1.8271227498804808e-17,0.7833269096274832,-0.6216099682706642],[5.241803274357495e-17,0.6216099682706642,0.7833269096274832]],\"depth_convention\":\"negative_camera_z_optical_depth\",\"systematic_sigma_m\":0.01,\"pose_source\":\"ideal_simulated_odometry\",\"mount_height_m\":1.2},\"base_pose\":{\"x_m\":0,\"y_m\":-1.5,\"yaw_rad\":1.5707963267948966,\"frame_id\":\"house_ground_v4\",\"source\":\"ideal drift-free simulator odometry\"},\"arm_coordinate_frame\":\"arm_carriage\",\"lidar_2d\":{\"frame_id\":\"base_link\",\"height_m\":0.15,\"angle_min_rad\":-3.141592653589793,\"angle_increment_rad\":0.08726646259971647,\"range_max_m\":6,\"ranges_m\":[0.931,0.944,0.948,0.97,0.996,1.031,1.09,1.142,1.218,1.332,1.461,1.635,1.874,2.222,2.747,3.256,3.558,3.519,2.025,2.031,2.068,2.098,2.158,2.236,2.336,2.468,2.651,2.663,3.153,6,6,5.887,6,4.221,5.428,6,6,2.881,6,3.553,0.664,0.535,0.464,0.435,0.453,0.498,0.534,0.609,0.708,2.882,2.766,2.878,2.819,2.795,2.772,2.792,3.258,2.969,2.24,1.814,1.54,1.345,1.19,1.08,1.169,1.149,1.085,1.022,0.998,0.967,0.945,0.948],\"source\":\"simulated horizontal ray range sensor; 5 mm noise\"},\"camera_head\":{\"pan_rad\":0,\"tilt_rad\":-0.9},\"peak_force_N\":0,\"lift_m\":0,\"door_grip\":null}",
      "tool_call_id": "call_IByW2cdYcZqq4rKBWBZsxfia"
    },
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Attached image(s) from tool result:"
        },
        {
          "type": "image_url",
          "image_url": {
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    {
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      "content": [
        {
          "type": "text",
          "text": "Attached image(s) from tool result:"
        },
        {
          "type": "image_url",
          "image_url": {
            "url": 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        {
          "type": "text",
          "text": "Attached image(s) from tool result:"
        },
        {
          "type": "image_url",
          "image_url": {
            "url": 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"
          }
        }
      ]
    },
    {
      "role": "assistant",
      "content": "",
      "tool_calls": [
        {
          "id": "call_px78Gr01BGUshBiVT8rvBvy4",
          "type": "function",
          "function": {
            "name": "room_measure",
            "arguments": "{\"i\":\"Zaktualizuj pomiary na bazie trzeciej obserwacji.\",\"expected_revision\":2}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"memory_revision\":3,\"dimensions\":{\"width\":{\"value_m\":null,\"status\":\"opposing_planes_not_observed\"},\"length\":{\"value_m\":null,\"status\":\"opposing_planes_not_observed\"},\"height\":{\"value_m\":null,\"status\":\"floor_or_ceiling_not_observed\"}},\"coverage\":{\"dimensions_complete\":false,\"full_room_coverage_verified\":false,\"localization\":\"ideal drift-free simulated odometry, not learned SLAM\"},\"walls_this_view\":[],\"walls_in_memory\":[],\"horizontal_planes\":{\"floor\":{\"offset_m\":0.0006778148853665567,\"points\":6081}},\"visible_objects\":[\"collection_bin\"]}",
      "tool_call_id": "call_px78Gr01BGUshBiVT8rvBvy4"
    }
  ],
  "stream": true,
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "robot_capabilities",
        "description": "Discover complete v4 robot, optional lift, carriage frame, limits and floor identity.",
        "parameters": {
          "properties": {
            "i": {
              "description": "concise intent",
              "type": "string"
            }
          },
          "required": [
            "i"
          ],
          "type": "object",
          "additionalProperties": false
        },
        "strict": true
      }
    },
    {
      "type": "function",
      "function": {
        "name": "robot_observe",
        "description": "Fresh mounted RGB-D, visible door/handle masks and noisy handle candidate, lidar, force and odometry.",
        "parameters": {
          "properties": {
            "i": {
              "description": "concise intent",
              "type": "string"
            }
          },
          "required": [
            "i"
          ],
          "type": "object",
          "additionalProperties": false
        },
        "strict": true
      }
    },
    {
      "type": "function",
      "function": {
        "name": "robot_execute_sequence",
        "description": "Same four literal arm calls; v4 arm_carriage z is relative to current lift. No arbitrary Python.",
        "parameters": {
          "properties": {
            "i": {
              "description": "concise intent",
              "type": "string"
            },
            "script": {
              "type": "string"
            },
            "observation_id": {
              "type": "string"
            },
            "command_id": {
              "type": "string"
            }
          },
          "required": [
            "i",
            "script",
            "observation_id",
            "command_id"
          ],
          "type": "object",
          "additionalProperties": false
        },
        "strict": true
      }
    },
    {
      "type": "function",
      "function": {
        "name": "robot_move_base",
        "description": "Same bounded relative drive; requires lift down, arm stowed, no door grip, clear supported floor.",
        "parameters": {
          "properties": {
            "i": {
              "description": "concise intent",
              "type": "string"
            },
            "dx_m": {
              "type": "number"
            },
            "dy_m": {
              "type": "number"
            },
            "dyaw_rad": {
              "type": "number"
            },
            "observation_id": {
              "type": "string"
            },
            "command_id": {
              "type": "string"
            }
          },
          "required": [
            "i",
            "dx_m",
            "dy_m",
            "dyaw_rad",
            "observation_id",
            "command_id"
          ],
          "type": "object",
          "additionalProperties": false
        },
        "strict": true
      }
    },
    {
      "type": "function",
      "function": {
        "name": "robot_look",
        "description": "Same absolute pan/tilt relative to base. Observe again after motion.",
        "parameters": {
          "properties": {
            "i": {
              "description": "concise intent",
              "type": "string"
            },
            "pan_rad": {
              "type": "number"
            },
            "tilt_rad": {
              "type": "number"
            },
            "observation_id": {
              "type": "string"
            },
            "command_id": {
              "type": "string"
            }
          },
          "required": [
            "i",
            "pan_rad",
            "tilt_rad",
            "observation_id",
            "command_id"
          ],
          "type": "object",
          "additionalProperties": false
        },
        "strict": true
      }
    },
    {
      "type": "function",
      "function": {
        "name": "robot_move_lift",
        "description": "Optional arm carriage lift0..0.45m, empty stowed arm only; base interlocked while raised.",
        "parameters": {
          "properties": {
            "i": {
              "description": "concise intent",
              "type": "string"
            },
            "height_m": {
              "type": "number"
            },
            "observation_id": {
              "type": "string"
            },
            "command_id": {
              "type": "string"
            }
          },
          "required": [
            "i",
            "height_m",
            "observation_id",
            "command_id"
          ],
          "type": "object",
          "additionalProperties": false
        },
        "strict": true
      }
    },
    {
      "type": "function",
      "function": {
        "name": "robot_door_action",
        "description": "Grasp at visible handle using current TCP, press spring lever, physically swing latch-released hinge, or release. Bounded ideal contact controller.",
        "parameters": {
          "properties": {
            "i": {
              "description": "concise intent",
              "type": "string"
            },
            "door_id": {
              "type": "string"
            },
            "action": {
              "enum": [
                "grasp",
                "press",
                "open",
                "close",
                "release"
              ],
              "type": "string"
            },
            "observation_id": {
              "type": "string"
            },
            "command_id": {
              "type": "string"
            }
          },
          "required": [
            "i",
            "door_id",
            "action",
            "observation_id",
            "command_id"
          ],
          "type": "object",
          "additionalProperties": false
        },
        "strict": true
      }
    },
    {
      "type": "function",
      "function": {
        "name": "robot_stop",
        "description": "Latch stop without releasing held cloth or door handle.",
        "parameters": {
          "properties": {
            "i": {
              "description": "concise intent",
              "type": "string"
            }
          },
          "required": [
            "i"
          ],
          "type": "object",
          "additionalProperties": false
        },
        "strict": true
      }
    },
    {
      "type": "function",
      "function": {
        "name": "mission_memory_read",
        "description": "Read configured mission/room memory; may contain unverified prior agent notes.",
        "parameters": {
          "properties": {
            "i": {
              "description": "concise intent",
              "type": "string"
            }
          },
          "required": [
            "i"
          ],
          "type": "object",
          "additionalProperties": false
        },
        "strict": true
      }
    },
    {
      "type": "function",
      "function": {
        "name": "room_measure",
        "description": "Measure latest RGB-D observation and atomically update configured room memory using expected revision.",
        "parameters": {
          "properties": {
            "i": {
              "description": "concise intent",
              "type": "string"
            },
            "expected_revision": {
              "type": "integer"
            }
          },
          "required": [
            "i",
            "expected_revision"
          ],
          "type": "object",
          "additionalProperties": false
        },
        "strict": true
      }
    },
    {
      "type": "function",
      "function": {
        "name": "mission_memory_write",
        "description": "Save bounded mission notes: tracks, attempts, blocked reasons and evidence. These notes are not host-verified deliveries.",
        "parameters": {
          "properties": {
            "i": {
              "description": "concise intent",
              "type": "string"
            },
            "notes": {
              "type": "string"
            }
          },
          "required": [
            "i",
            "notes"
          ],
          "type": "object",
          "additionalProperties": false
        },
        "strict": true
      }
    }
  ]
}