# Marcus — System Architecture

**Project**: Marcus | YS Lootah Technology
**Hardware**: Unitree G1 EDU Humanoid (29 DOF) + Jetson Orin NX (16GB)
**Updated**: 2026-04-06

---

## Overview

Marcus is a fully offline humanoid robot AI system. The brain runs on Jetson Orin NX with no cloud dependencies. It uses vision-language models (Qwen2.5-VL via Ollama) for understanding commands, YOLO for real-time object detection, dead reckoning for position tracking, and persistent memory across sessions.

Two operating modes:
- **Terminal mode** (`run_marcus.py`) — direct keyboard control on the Jetson
- **Server mode** (`Server/marcus_server.py`) — WebSocket server allowing remote CLI or GUI clients

Both modes use the **same brain** — identical command processing, same YOLO, same memory, same movement control.

---

## Project Structure

```
Marcus/
├── run_marcus.py                 # Entrypoint — terminal mode
├── .env                          # Machine-specific: PROJECT_BASE, PROJECT_NAME
│
├── Core/                         # Foundation layer — no external deps
│   ├── env_loader.py             # Reads .env, resolves PROJECT_ROOT
│   ├── config_loader.py          # load_config(name) → reads Config/config_{name}.json
│   ├── Logger.py                 # Logging engine (file-based, no console output)
│   └── logger.py                 # Project wrapper: log(), log_and_print(), get_logger()
│
├── Config/                       # ALL configuration — one JSON per module
│   ├── config_ZMQ.json           # ZMQ host, port, stop params
│   ├── config_Camera.json        # RealSense resolution, fps, quality
│   ├── config_Brain.json         # Ollama model, prompts, num_predict values
│   ├── config_Vision.json        # YOLO model path, confidence, tracked classes
│   ├── config_Navigation.json    # move_map, goal aliases, YOLO goal classes
│   ├── config_Patrol.json        # patrol duration, proximity threshold
│   ├── config_Arm.json           # arm actions, aliases, availability flag
│   ├── config_Odometry.json      # speeds, tolerances, ROS2 topic
│   ├── config_Memory.json        # session/places paths
│   ├── config_Network.json       # Jetson IPs (eth0/wlan0), ports
│   ├── config_ImageSearch.json   # search defaults
│   └── marcus_prompts.yaml       # All LLaVA/Qwen prompts (main, goal, patrol, talk, verify)
│
├── API/                          # Interface layer — one file per subsystem
│   ├── zmq_api.py                # ZMQ PUB socket: send_vel(), gradual_stop(), send_cmd()
│   ├── camera_api.py             # RealSense thread: start/stop_camera(), get_frame()
│   ├── llava_api.py              # LLaVA queries: call_llava(), ask(), ask_goal(), ask_patrol()
│   ├── yolo_api.py               # YOLO interface: init_yolo(), yolo_sees(), yolo_summary()...
│   ├── odometry_api.py           # Odometry wrapper: init_odometry(), get_position()
│   ├── memory_api.py             # Memory wrapper: init_memory(), log_cmd(), place_save/goto()
│   ├── arm_api.py                # Arm gestures: do_arm(), ARM_ACTIONS, ALL_ARM_NAMES
│   └── imgsearch_api.py          # Image search wrapper: init_imgsearch(), get_searcher()
│
├── Brain/                        # Decision logic — imports ONLY from API/
│   ├── marcus_brain.py           # Orchestrator: init_brain(), process_command(), run_terminal()
│   ├── command_parser.py         # 14 regex patterns + try_local_command() dispatcher
│   ├── executor.py               # execute_action(), merge_actions(), execute()
│   └── marcus_memory.py          # Session + place memory (Memory class, 817 lines)
│
├── Navigation/                   # Movement + position tracking
│   ├── goal_nav.py               # navigate_to_goal() — YOLO+LLaVA hybrid visual search
│   ├── patrol.py                 # patrol() — autonomous HSE patrol with PPE detection
│   └── marcus_odometry.py        # Odometry class — dead reckoning + ROS2 fallback
│
├── Vision/                       # Computer vision
│   ├── marcus_yolo.py            # YOLO background inference: Detection class + query API
│   └── marcus_imgsearch.py       # ImageSearch class — reference image comparison
│
├── Server/                       # WebSocket server (runs on Jetson)
│   └── marcus_server.py          # Full brain over WebSocket — same as run_marcus.py
│
├── Client/                       # Remote clients (run on workstation)
│   ├── marcus_cli.py             # Terminal CLI client with color output
│   └── marcus_client.py          # Tkinter GUI client (3 tabs: Nav/Camera/LiDAR)
│
├── Bridge/                       # ROS2 integration
│   └── ros2_zmq_bridge.py        # ROS2 /cmd_vel → ZMQ velocity bridge
│
├── Autonomous/                   # Autonomous exploration mode
│   └── marcus_autonomous.py      # AutonomousMode — office exploration + mapping
│
├── Models/                       # AI model weights
│   ├── yolov8m.pt                # YOLOv8 medium (50MB)
│   └── Modelfile                 # Ollama model definition (FROM qwen2.5vl:7b)
│
├── Data/                         # Runtime-generated data ONLY (no code)
│   ├── Brain/Sessions/           # session_{id}_{date}/ — commands, detections, alerts
│   ├── Brain/Exploration/        # Autonomous mode map data
│   ├── History/Places/           # places.json — persistent named locations
│   ├── History/Sessions/         # Session history
│   ├── History/Prompts/          # Prompt history
│   ├── Navigation/Maps/          # SLAM occupancy grids
│   ├── Navigation/Waypoints/     # Saved waypoint files
│   ├── Vision/Camera/            # Captured camera frames
│   ├── Vision/Videos/            # Recorded video clips
│   └── Vision/Frames/            # Detection snapshots
│
├── Doc/                          # Documentation
│   ├── architecture.md           # This file
│   ├── controlling.md            # Startup guide + command reference
│   ├── MARCUS_API.md             # API reference
│   └── note.txt                  # Quick notes
│
├── logs/                         # Runtime logs (one per module)
│   ├── brain.log
│   ├── camera.log
│   ├── server.log
│   ├── zmq.log
│   └── main.log
│
└── Legacy/                       # Archived originals
    └── marcus_nav.py             # Original standalone prototype
```

---

## Layer Architecture

```
┌─────────────────────────────────────────────────┐
│                  Entrypoints                     │
│  run_marcus.py (terminal)                        │
│  Server/marcus_server.py (WebSocket)             │
└──────────────────┬──────────────────────────────┘
                   │
┌──────────────────▼──────────────────────────────┐
│                Brain Layer                       │
│  marcus_brain.py    — init_brain()               │
│                     — process_command(cmd)        │
│  command_parser.py  — 14 regex local commands    │
│  executor.py        — execute LLaVA decisions    │
│  marcus_memory.py   — session + place memory     │
└──────────────────┬──────────────────────────────┘
                   │ imports only from API/
┌──────────────────▼──────────────────────────────┐
│                 API Layer                        │
│  zmq_api     camera_api    llava_api            │
│  yolo_api    odometry_api  memory_api           │
│  arm_api     imgsearch_api                      │
└──────────────────┬──────────────────────────────┘
                   │ wraps
┌──────────────────▼──────────────────────────────┐
│            Navigation / Vision                   │
│  goal_nav.py        marcus_yolo.py              │
│  patrol.py          marcus_imgsearch.py         │
│  marcus_odometry.py                              │
└──────────────────┬──────────────────────────────┘
                   │
┌──────────────────▼──────────────────────────────┐
│               Core Layer                         │
│  env_loader.py   config_loader.py               │
│  Logger.py       logger.py                      │
└──────────────────┬──────────────────────────────┘
                   │ reads
┌──────────────────▼──────────────────────────────┐
│              Config / .env                       │
│  11 JSON files + marcus_prompts.yaml            │
└─────────────────────────────────────────────────┘
```

**Rule**: Brain never imports from Vision/ or Navigation/ directly. It goes through the API layer.

---

## File-by-File Documentation

### Core/

#### `env_loader.py` (34 lines)
Reads `.env` from the project root to resolve `PROJECT_ROOT`. Uses a minimal built-in parser (no `python-dotenv` dependency). Exports `PROJECT_ROOT` as a `Path` object resolved from `__file__`, so it works regardless of where the script is called from. Fallback default: `/home/unitree`.

#### `config_loader.py` (30 lines)
`load_config(name)` reads `Config/config_{name}.json` and caches the result. All modules call this instead of hardcoding constants. Also provides `config_path(relative)` to resolve relative paths (e.g., `"Models/yolov8m.pt"`) to absolute paths from PROJECT_ROOT.

#### `Logger.py` (186 lines)
Full logging engine from AI_Photographer. File-based only (no console output by default). Creates per-module log files in `logs/`. Handles write permission fallbacks, log name normalization, and corrupt log recovery.

#### `logger.py` (51 lines)
Project wrapper around `Logger.py`. Provides:
- `log(message, level, module)` — write to `logs/{module}.log`
- `log_and_print(message, level, module)` — write + print
- `get_logger(module)` — get configured Logs instance

---

### API/

Each API file wraps one subsystem. They read their own config via `load_config()`, handle import errors gracefully with fallback stubs, and export clean public functions.

#### `zmq_api.py` (49 lines)
Creates a ZMQ PUB socket on startup (binds to `tcp://127.0.0.1:{zmq_port}`). Holosoma's RL policy connects to this socket as SUB and receives velocity commands at 50Hz.

**Exports:**
- `send_vel(vx, vy, vyaw)` — send velocity to Holosoma
- `gradual_stop()` — 20 zero-velocity messages over 1 second
- `send_cmd(cmd)` — send state command: "start", "walk", "stand", "stop"
- `get_socket()` — return the shared PUB socket (for odometry to reuse)
- `MOVE_MAP` — direction-to-velocity lookup: `{"forward": (0.3, 0, 0), "left": (0, 0, 0.3), ...}`

**Config:** `config_ZMQ.json` — host, port, stop_iterations, stop_delay, step_pause

#### `camera_api.py` (111 lines)
Background thread captures RealSense D435I frames continuously. Stores both raw BGR (for YOLO) and base64 JPEG (for LLaVA). Auto-reconnects on USB drops with exponential backoff (2s → 4s → 8s, max 10s).

**Exports:**
- `start_camera()` — starts thread, returns `(raw_frame_ref, raw_lock)` for YOLO
- `stop_camera()` — stops the thread
- `get_frame()` — returns latest base64 JPEG (or last known good frame)
- `get_frame_age()` — seconds since last successful frame
- `get_raw_refs()` — returns shared numpy frame + lock for YOLO

**Config:** `config_Camera.json` — width (424), height (240), fps (15), jpeg_quality (70)

#### `llava_api.py` (107 lines)
Interface to Ollama's vision-language model (Qwen2.5-VL 3B). Manages conversation history (6-turn sliding window) and user-told facts for context injection.

**Exports:**
- `call_llava(prompt, img_b64, num_predict, use_history)` — raw LLM call
- `ask(command, img_b64)` — send command + image, get structured JSON response
- `ask_goal(goal, img_b64)` — check if goal reached during navigation
- `ask_patrol(img_b64)` — assess scene during autonomous patrol
- `parse_json(raw)` — extract JSON from LLM output
- `add_to_history(user_msg, assistant_msg)` — add to conversation context
- `remember_fact(fact)` — store persistent fact (e.g., "Kassam is the programmer")
- `OLLAMA_MODEL` — current model name from config

**Config:** `config_Brain.json` — ollama_model, max_history, num_predict values, prompts

#### `yolo_api.py` (66 lines)
Lazy-loads YOLO from `Vision/marcus_yolo.py`. If import fails, all functions return safe defaults (empty sets, False, 0). No crash on missing dependencies.

**Exports:**
- `init_yolo(raw_frame_ref, frame_lock)` — start background inference
- `yolo_sees(class_name)` — is class currently detected?
- `yolo_count(class_name)` — how many instances?
- `yolo_closest(class_name)` — nearest Detection object
- `yolo_summary()` — human-readable summary: "2 persons (left, close) | 1 chair"
- `yolo_ppe_violations()` — list of PPE violations
- `yolo_person_too_close(threshold)` — safety proximity check
- `yolo_all_classes()` — set of all currently detected classes
- `yolo_fps()` — current inference rate
- `YOLO_AVAILABLE` — True if YOLO loaded successfully

#### `odometry_api.py` (40 lines)
Wraps `Navigation/marcus_odometry.py`. Passes the shared ZMQ socket to avoid port conflicts.

**Exports:**
- `init_odometry(zmq_sock)` — start tracking, returns success bool
- `get_position()` — returns `{"x": float, "y": float, "heading": float, "source": str}`
- `odom` — the Odometry instance (or None)
- `ODOM_AVAILABLE` — True if running

#### `memory_api.py` (109 lines)
Wraps `Brain/marcus_memory.py`. Also contains place memory functions that combine memory + odometry.

**Exports:**
- `init_memory()` — start session, load places
- `log_cmd(cmd, response, duration)` — log command to session
- `log_detection(class_name, position, distance)` — log YOLO detection with odometry position
- `place_save(name)` — save current position as named place
- `place_goto(name)` — navigate to saved place using odometry
- `places_list_str()` — formatted table of all saved places
- `mem` — Memory instance (or None)
- `MEMORY_AVAILABLE` — True if running

#### `arm_api.py` (16 lines)
Stub for GR00T N1.5 arm control. Currently prints a message. ARM_ACTIONS and ARM_ALIASES loaded from `config_Arm.json`.

**Exports:**
- `do_arm(action)` — execute arm gesture (currently stub)
- `ARM_ACTIONS` — dict of action name → action ID
- `ARM_ALIASES` — dict of common names → action ID
- `ALL_ARM_NAMES` — set of all recognized arm command names
- `ARM_AVAILABLE` — False (pending GR00T integration)

#### `imgsearch_api.py` (38 lines)
Wraps `Vision/marcus_imgsearch.py`. Wires camera, ZMQ, LLaVA, and YOLO into the ImageSearch class.

**Exports:**
- `init_imgsearch(get_frame_fn, send_vel_fn, ...)` — wire dependencies
- `get_searcher()` — return ImageSearch instance (or None)

---

### Brain/

#### `marcus_brain.py` (372 lines)
**The orchestrator.** Contains all the brain's public functions used by both terminal and server modes.

**Key functions:**
- `init_brain()` — initializes all subsystems in order: camera → YOLO → odometry → memory → image search → Holosoma boot → LLaVA warmup
- `process_command(cmd) → dict` — routes a command through the full pipeline and returns `{"type", "speak", "action", "elapsed"}`. Pipeline order:
  1. YOLO status check
  2. Image search (`search/`)
  3. Natural language goal auto-detect ("find a person", "look for a bottle")
  4. Explicit goal (`goal/ ...`)
  5. Patrol (`patrol`)
  6. Local commands (place memory, odometry, help) via `command_parser.py`
  7. Talk-only questions (what/who/where/how)
  8. Greetings (hi/hello/salam) — instant, no AI
  9. "Come to me" shortcut — instant forward 2s
  10. Multi-step compound ("turn right then walk forward")
  11. Standard LLaVA command — full AI inference
- `run_terminal()` — terminal input loop (used by `run_marcus.py`)
- `get_brain_status()` — returns dict of all subsystem states
- `shutdown()` — clean stop of all subsystems

#### `command_parser.py` (300 lines)
14 compiled regex patterns that intercept commands before they reach LLaVA. Handles:

| Pattern | Example | Action |
|---------|---------|--------|
| `_RE_REMEMBER` | "remember this as door" | Save current position |
| `_RE_GOTO` | "go to door" | Navigate to saved place |
| `_RE_FORGET` | "forget door" | Delete saved place |
| `_RE_RENAME` | "rename door to entrance" | Rename place |
| `_RE_WALK_DIST` | "walk 1 meter" | Precise odometry walk |
| `_RE_WALK_BACK` | "walk backward 2 meters" | Precise backward walk |
| `_RE_TURN_DEG` | "turn right 90 degrees" | Precise odometry turn |
| `_RE_PATROL_RT` | "patrol: door → desk → exit" | Named waypoint patrol |
| `_RE_LAST_CMD` | "last command" | Recall from session |
| `_RE_DO_AGAIN` | "do that again" | Repeat last command |
| `_RE_UNDO` | "undo" | Reverse last movement |
| `_RE_LAST_SESS` | "last session" | Previous session summary |
| `_RE_WHERE` | "where am I" | Current odometry position |
| `_RE_GO_HOME` | "go home" | Return to start position |

Also handles: session summary, help text, examples text.

#### `executor.py` (81 lines)
Executes LLaVA movement decisions. Converts the JSON action list into sustained ZMQ velocity commands.

**Functions:**
- `execute_action(move, duration)` — single movement step. Uses `MOVE_MAP` for velocities, intercepts arm names that LLaVA sometimes puts in the actions list
- `move_step(move, duration)` — lightweight version for goal/patrol loops (no full gradual_stop between steps)
- `merge_actions(actions)` — combines consecutive same-direction steps: 5x right 1.0s → 1x right 5.0s
- `execute(d)` — full decision execution: movements in sequence, arm gesture in background thread

#### `marcus_memory.py` (817 lines)
Persistent session and place memory. Thread-safe with atomic JSON writes.

**Place memory:**
- Save named positions with odometry coordinates
- Fuzzy name matching (typo tolerance)
- Name sanitization (special chars → underscores)
- Rename, delete, list operations

**Session memory:**
- Per-session folders: `session_{id}_{date}/`
- Logs: commands.json, detections.json, alerts.json, summary.txt
- 60-second auto-flush in background thread
- Emergency save via `atexit` on crash
- YOLO detection deduplication (5-second window)
- Cross-session recall ("what did you do last session?")
- Auto-prune old sessions (keeps last 50)

---

### Navigation/

#### `goal_nav.py` (154 lines)
Visual goal navigation. Robot rotates continuously while scanning for a target using YOLO (fast, 0.4s checks) with LLaVA fallback (slow but handles non-YOLO classes).

**How it works:**
1. Parse goal to extract YOLO target class (via aliases: "guy" → "person", "sofa" → "couch")
2. Start continuous rotation in background thread
3. YOLO fast-check every 0.4s — if target class found:
   - Extract compound condition ("holding a phone", "wearing red")
   - If compound: ask LLaVA to verify ("Is the person holding a phone? yes/no")
   - If verified (or no compound): stop and report
4. LLaVA fallback for non-YOLO classes: send goal_prompt with image, check if `reached: true`
5. Max steps limit (40 default), Ctrl+C to abort

**Config:** `config_Navigation.json` — goal_aliases, yolo_goal_classes, max_steps, rotation_speed

#### `patrol.py` (106 lines)
Autonomous HSE inspection patrol. Timed loop with YOLO PPE detection and LLaVA scene assessment.

**How it works:**
1. YOLO checks for PPE violations (no helmet, no vest) and logs alerts
2. Safety: stop if person too close (size_ratio > 0.3)
3. LLaVA assesses scene: observation, alert, next_move, duration
4. Executes lightweight movement steps between checks
5. All detections and alerts logged to session memory

**Config:** `config_Patrol.json` — default_duration_minutes, proximity_threshold

#### `marcus_odometry.py` (808 lines)
Precise position tracking and movement control.

**Dual source** (priority order):
1. ROS2 `/dog_odom` — joint encoder data, ±2cm accuracy (currently disabled due to DDS memory conflict)
2. Dead reckoning — velocity × time integration at 20Hz, ±10cm accuracy

**Movement API:**
- `walk_distance(meters, speed, direction)` — odometry feedback loop, 5cm tolerance, safety timeout
- `turn_degrees(degrees, speed)` — heading feedback with 0°/360° wrap-around, 2° tolerance
- `navigate_to(x, y, heading)` — rotate to face target, walk straight, rotate to final heading
- `return_to_start()` — navigate back to where `start()` was called
- `patrol_route(waypoints, loop)` — walk through list of waypoints in order

All movements have time-based fallbacks when odometry isn't running. Speed clamped at 0.4 m/s. KeyboardInterrupt handling with gradual stop.

---

### Vision/

#### `marcus_yolo.py` (474 lines)
Background YOLO inference engine. Runs in a daemon thread, reads from the shared camera frame buffer.

**Detection class:** Each detection has class_name, confidence, bbox, position (left/center/right), distance_estimate (very close/close/medium/far), size_ratio.

**Public API:**
- `start_yolo(raw_frame_ref, frame_lock)` — start inference thread
- `yolo_sees(class_name, min_confidence)` — check if class detected
- `yolo_count(class_name)` — count instances
- `yolo_closest(class_name)` — largest bbox (closest object)
- `yolo_summary()` — "2 persons (left, close) | 1 chair (center, medium)"
- `yolo_ppe_violations()` — PPE-specific detections
- `yolo_person_too_close(threshold)` — safety proximity check

**Config:** `config_Vision.json` — model path, confidence (0.45), 19 tracked COCO classes

#### `marcus_imgsearch.py` (501 lines)
Image-guided search. User provides a reference photo; robot rotates and LLaVA compares camera frames to the reference.

**How it works:**
1. Load reference image (resize to 336x336 for efficiency)
2. Start continuous rotation
3. Optional YOLO pre-filter (find "person" class before running LLaVA)
4. LLaVA comparison: sends [reference, current_frame] as two images
5. Parse JSON response: found, confidence (low/medium/high), position, description
6. Stop on medium/high confidence match

Supports text-only search (no reference image) using hint description.

---

### Server/

#### `marcus_server.py` (224 lines)
WebSocket server that wraps the full Marcus brain. Initializes all subsystems (camera, YOLO, odometry, memory, LLaVA) on startup, then accepts commands via WebSocket.

**Architecture:**
- Calls `init_brain()` from `marcus_brain.py` — same init as terminal mode
- Each incoming `"command"` message runs `process_command(cmd)` in a thread pool
- Broadcasts camera frames to all clients at ~10Hz
- Auto-detects eth0 and wlan0 IPs for the connection banner

**WebSocket message types:**
| Client sends | Server responds |
|---|---|
| `{"type": "command", "command": "turn left"}` | `{"type": "thinking"}` then `{"type": "decision", "action": "LEFT", "speak": "Turning left", ...}` |
| `{"type": "capture"}` | `{"type": "capture_result", "ok": true, "data": "<base64>"}` |
| `{"type": "ping"}` | `{"type": "pong", "lidar": true, "status": {...}}` |

**Config:** `config_Network.json` — jetson_ip, jetson_wlan_ip, websocket_port

---

### Client/

#### `marcus_cli.py` (288 lines)
Terminal CLI client for remote control. Connects to the server via WebSocket.

**Features:**
- Connection menu: choose eth0 / wlan0 / custom IP
- Color-coded output: green=forward, cyan=turn, red=stop, orange=greeting/local
- Displays `Marcus: <speak text>` for every response
- System commands: `status`, `camera`, `profile <name>`, `capture`, `help`, `q`
- Async receiver for real-time decision display while typing
- Command history (not persisted)

#### `marcus_client.py` (1021 lines)
Tkinter GUI client with 3 tabs:
- **Navigation** — live camera view, command entry, quick buttons, decision log
- **Camera** — profile switcher, custom resolution, capture, preview toggle
- **LiDAR** — full SLAM Commander (runs locally via SlamEngineClient from G1_Lootah/Lidar)

---

### Bridge/

#### `ros2_zmq_bridge.py` (66 lines)
ROS2 Foxy node that subscribes to `/cmd_vel` (TwistStamped) and `holosoma/other_input` (String), forwarding to the ZMQ PUB socket. Requires Python 3.8 + ROS2 sourced. Used when external ROS2 nodes need to send velocity commands to Holosoma.

---

### Autonomous/

#### `marcus_autonomous.py` (516 lines)
Autonomous office exploration mode. Marcus moves freely, identifies areas and objects, builds a live map, saves everything to a session folder.

**State machine:** IDLE → EXPLORING → IDLE

**Exploration loop:**
1. Safety: stop if person too close
2. Record YOLO detections + odometry path point
3. Every 5 steps: LLaVA scene assessment (area_type, objects, observation)
4. Move forward; turn when blocked (alternates left/right)
5. Save interesting frames to disk
6. Auto-flush to disk every 20 steps

**Output:** `Data/Brain/Exploration/map_{id}_{date}/` — observations.json, path.json, summary.txt, frames/

---

## Data Flow

### Command: "turn right"

```
User types "turn right"
  │
  ▼
process_command("turn right")
  │ (no regex match — falls through to LLaVA)
  ▼
llava_api.ask("turn right", camera_frame)
  │ sends to Ollama qwen2.5vl:3b
  ▼
LLaVA returns: {"actions":[{"move":"right","duration":2.0}], "speak":"Turning right"}
  │
  ▼
executor.execute(d)
  │ merge_actions → execute_action("right", 2.0)
  ▼
zmq_api.send_vel(vyaw=-0.3) × 40 times over 2.0 seconds
  │
  ▼
Holosoma RL policy receives velocity → robot turns right
  │
  ▼
zmq_api.gradual_stop() → 20 zero-velocity messages
```

### Command: "remember this as door"

```
User types "remember this as door"
  │
  ▼
process_command("remember this as door")
  │ matches _RE_REMEMBER regex
  ▼
command_parser.try_local_command()
  │ calls memory_api.place_save("door")
  ▼
odometry_api.get_position() → {"x": 1.2, "y": 0.5, "heading": 90.0}
  │
  ▼
marcus_memory.Memory.save_place("door", x=1.2, y=0.5, heading=90.0)
  │ atomic write to Data/History/Places/places.json
  ▼
Returns: {"type": "local", "speak": "Done", "action": "LOCAL"}
```

### Command: "goal/ find a person"

```
User types "goal/ find a person"
  │
  ▼
process_command() → navigate_to_goal("find a person")
  │
  ▼
_goal_yolo_target("find a person") → "person"
  │ YOLO mode (not LLaVA fallback)
  ▼
Start continuous rotation thread (vyaw=0.3)
  │
  ▼
Loop every 0.4s:
  │ yolo_sees("person") → False → keep rotating
  │ yolo_sees("person") → False → keep rotating
  │ yolo_sees("person") → True!
  │   ▼
  │   _extract_extra_condition() → None (no compound)
  │   ▼
  │   gradual_stop()
  │   yolo_closest("person") → Detection(center, close)
  │   log_detection("person", "center", "close")
  ▼
Returns: {"type": "goal", "speak": "Goal navigation: find a person"}
```

---

## Hardware Stack

```
Unitree G1 EDU (29 DOF)
  │
  ├── Jetson Orin NX (16GB VRAM)
  │     ├── Holosoma RL policy (50Hz) — locomotion joints 0-11
  │     ├── Ollama + Qwen2.5-VL 3B — vision-language understanding
  │     ├── YOLOv8m — real-time object detection (CPU, 320px)
  │     └── Marcus Brain — this project
  │
  ├── RealSense D435I — RGB camera (424x240 @ 15fps)
  │
  ├── Livox Mid360 LiDAR — 3D point cloud (via SlamEngineClient)
  │
  └── ZMQ PUB/SUB — velocity commands (tcp://127.0.0.1:5556)
        ├── Marcus Brain PUB → Holosoma SUB
        └── ROS2 Bridge PUB → Holosoma SUB (alternative)
```

---

## Startup Order

1. **Holosoma** — must be running first (RL locomotion policy)
2. **Marcus Server** (`python3 -m Server.marcus_server`) — or Brain (`python3 run_marcus.py`)
3. **Client** (`python3 -m Client.marcus_cli`) — connects to server

Cannot run Server and Brain simultaneously (both bind ZMQ port 5556).

---

## Config Reference

| File | Key values |
|------|-----------|
| `config_ZMQ.json` | zmq_host: 127.0.0.1, zmq_port: 5556 |
| `config_Camera.json` | 424x240 @ 15fps, JPEG quality 70 |
| `config_Brain.json` | qwen2.5vl:3b, history 6 turns, prompts |
| `config_Vision.json` | yolov8m.pt, confidence 0.45, 19 classes |
| `config_Navigation.json` | move_map velocities, goal aliases |
| `config_Network.json` | eth0: 192.168.123.164, wlan0: 10.255.254.86, port 8765 |
| `config_Odometry.json` | walk 0.25 m/s, turn 0.25 rad/s, 5cm tolerance |
| `config_Memory.json` | Data/Brain/Sessions, Data/History/Places |
| `config_Patrol.json` | 5 min default, proximity 0.3 |
| `config_Arm.json` | 16 gestures, arm_available: false (GR00T pending) |

---

## Line Count Summary

| Layer | Files | Lines |
|-------|-------|-------|
| Core | 4 | 301 |
| API | 8 | 536 |
| Brain | 4 | 1,570 |
| Navigation | 3 | 1,068 |
| Vision | 2 | 975 |
| Server | 1 | 224 |
| Client | 2 | 1,309 |
| Bridge | 1 | 66 |
| Autonomous | 1 | 516 |
| Entrypoint | 1 | 16 |
| **Total** | **27** | **6,581** |