188 lines
7.4 KiB
Markdown
188 lines
7.4 KiB
Markdown
# Marcus — End-to-End Pipeline
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**Robot persona:** Sanad (wake word + self-intro)
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**Updated:** 2026-04-21
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One map of every data path from sensor to motor, voice to speech. Cross-reference with `architecture.md` (what each file is) and `MARCUS_API.md` (function signatures).
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---
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## Boot sequence
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`Brain/marcus_brain.py::init_brain()` — called once from `run_marcus.py` or `marcus_server.py`.
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```
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run_marcus.py
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│
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▼
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init_brain()
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│
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├─ init_zmq() PUB bind tcp://127.0.0.1:5556 → Holosoma
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├─ start_camera() RealSense 424×240@15fps → shared _raw_frame
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├─ init_yolo(raw_frame, raw_lock) YOLOv8m CUDA FP16, 19 classes — background thread
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├─ init_odometry() ROS2 /dog_odom → dead reckoning fallback
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├─ init_memory() loads Data/Brain/Sessions/session_NNN/
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│
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├─ if subsystems.lidar: init_lidar() multiprocessing spawn SLAM_worker
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├─ if subsystems.imgsearch: init_imgsearch() (off by default)
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├─ if subsystems.autonomous: AutonomousMode() patrol state machine
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│
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├─ send_cmd("start") + 0.5s + send_cmd("walk") + 0.5s Holosoma handshake
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│
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├─ if subsystems.voice: _init_voice() ▼ voice pipeline below
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└─ _warmup_llava() first Qwen2.5-VL inference
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"SANAD AI BRAIN — READY"
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```
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Subsystem flags live in `config_Brain.json::subsystems`. Current defaults:
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```json
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"subsystems": { "lidar": true, "voice": true, "imgsearch": false, "autonomous": true }
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```
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---
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## Voice pipeline (when `subsystems.voice = true`)
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```
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G1 body mic (array)
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└─ UDP multicast 239.168.123.161:5555 ── int16 mono 16 kHz PCM
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▼
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Voice/builtin_mic.py::BuiltinMic
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ring buffer (64 KB) + read_chunk(n)
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▼
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Voice/marcus_voice.py::VoiceModule (IDLE → WAKE_HEARD → PROCESSING → SPEAKING)
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├─ IDLE : 2-s chunks → Whisper tiny → wake-word match ("sanad"/"sannad"/…)
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├─ WAKE_HEARD : audio_api.speak("Listening") → G1 body speaker
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├─ PROCESSING : record-until-silence → Whisper small → transcribed text
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└─ on_command(text, "en")
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▼
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Brain/marcus_brain.py::process_command(text)
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├─ regex fast-path → Brain/command_parser.py::try_local_command()
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│ places · odometry walk/turn · patrol · session recall · goal_nav · auto on/off
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└─ else → _handle_llava(text)
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├─ get_frame() (10×50 ms poll, no 1 s stall)
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├─ API/llava_api.py::ask(text, img)
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│ ollama.chat(qwen2.5vl:3b, num_batch=128, num_ctx=2048, num_predict=120)
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│ → parse_json() → {actions, arm, speak, abort}
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└─ Brain/executor.py::execute(d)
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├─ actions → API/zmq_api.py::send_vel(vx, vy, vyaw) → Holosoma
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├─ arm → API/arm_api.py (stub for now)
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└─ abort → gradual_stop()
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▼
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result["speak"] → audio_api.speak(reply)
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▼
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API/audio_api.py::speak(text, lang="en")
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├─ mute mic (flush BuiltinMic buffer)
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├─ Voice/builtin_tts.py::BuiltinTTS.speak(text)
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│ client.TtsMaker(text, speaker_id=0) — G1 on-board engine, English only
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│ time.sleep(len(text) * 0.08)
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└─ unmute mic → back to IDLE
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```
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---
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## Terminal / WebSocket command pipeline (same brain, skips voice)
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```
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run_marcus.py stdin OR Server/marcus_server.py WebSocket
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▼
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Brain/marcus_brain.py::process_command(text)
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▼ (same parser → LLaVA → executor → ZMQ as above)
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▼
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result dict → stdout OR WebSocket reply frame
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```
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---
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## Vision pipeline (continuous, consumed by brain on demand)
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```
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RealSense D435 (USB)
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└─ 424×240 BGR 15 fps
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→ API/camera_api.py — shared _raw_frame (thread-safe)
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│ │
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│ └─ get_frame() → JPEG base64 on demand
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▼
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Vision/marcus_yolo.py (daemon thread)
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YOLOv8m @ cuda:0 FP16 imgsz=320
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→ _latest_detections (thread-safe list)
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yolo_sees / yolo_closest / yolo_summary / yolo_fps
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▼
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Navigation/goal_nav.py (fast YOLO check → Qwen-VL fallback)
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Autonomous/marcus_autonomous.py (patrol scan every N steps)
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Brain/marcus_brain.py (status / alerts)
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```
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---
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## Movement pipeline
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```
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Brain/executor.py OR Brain/command_parser.py OR Navigation/*
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│ uses MOVE_MAP from config_Navigation.json
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▼
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API/zmq_api.py::send_vel(vx, vy, vyaw) JSON over ZMQ PUB (port 5556)
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▼
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Holosoma RL policy (separate process, hsinference env)
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▼
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G1 low-level joint commands over DDS/eth0
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▼
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29-DOF body motion
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```
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---
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## LiDAR pipeline (when `subsystems.lidar = true`)
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```
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Livox Mid-360 (192.168.123.120, UDP)
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▼
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Lidar/SLAM_worker.py (multiprocessing.spawn subprocess — CUDA-safe spawn)
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├─ SLAM_engine, SLAM_Filter, SLAM_LoopClosure, SLAM_Submap, SLAM_NavRuntime
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├─ publishes pose + obstacle flags back to parent via Queue
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└─ writes occupancy grids to Data/Navigation/Maps/
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▼
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API/lidar_api.py (reads the queues, exposes:)
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├─ obstacle_ahead() → bool
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├─ get_lidar_status() → dict (pose, loc_state, frame age, FPS, ICP ms)
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└─ LIDAR_AVAILABLE
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▼
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Navigation/goal_nav.py rotation thread — pauses motion on obstacle_ahead()
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Brain/command_parser.py — responds to "lidar status" queries
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```
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---
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## Knobs that control each stage
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| Knob | Location | Effect |
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| `subsystems.lidar` | config_Brain.json | SLAM subprocess on/off |
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| `subsystems.voice` | config_Brain.json | BuiltinMic + Whisper + TtsMaker loop on/off |
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| `subsystems.imgsearch` | config_Brain.json | image-guided search init on/off |
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| `subsystems.autonomous` | config_Brain.json | auto-patrol state machine init on/off |
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| `num_batch`, `num_ctx` | config_Brain.json | llama.cpp compute-graph size (128 / 2048 ≈ 1.8 GiB graph — **do not raise** on 16 GB Jetson) |
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| `num_predict_main` | config_Brain.json | 120 tokens max for the main JSON reply |
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| `yolo_device`, `yolo_half` | config_Vision.json | `cuda` / FP16 (hard-required; CPU not allowed) |
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| `mic.backend` | config_Voice.json | `builtin_udp` (G1 array) or `pactl_parec` (Hollyland fallback) |
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| `mic_udp.group/port` | config_Voice.json | where to join the G1 audio multicast |
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| `tts.backend` | config_Voice.json | `builtin_ttsmaker` (only supported option) |
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| `stt.wake_words_en` | config_Voice.json | Whisper matcher (`sanad` + variants) |
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---
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## Per-command latency (estimated, post-fixes)
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| Step | Typical | Notes |
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| Wake-word detect | 200–500 ms | Whisper tiny on 2 s chunk |
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| Record until silence | 1–8 s | depends on user speech |
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| Whisper small STT | 500–1500 ms | once per command |
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| Camera frame fetch | <50 ms | poll loop, no 1 s blocking stall |
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| Ollama Qwen2.5-VL | 800–1500 ms | `num_batch=128 / num_ctx=2048 / num_predict=120` |
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| Executor + ZMQ send | <10 ms | fire-and-forget PUB |
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| TtsMaker playback | ~len(text) × 80 ms | synthesizes + plays on robot |
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**Total wake → answer-playback:** ~**2.5–4 s** for a short vision question like "what do you see" (vs. 5–8 s with the pre-restructure edge-tts/Gemini overhead).
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