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  • SAM + XMEM // TRACKING
  • PERSISTENT IDENTITY
  • API-FIRST

MONAD

EVERY OBJECT, SOVEREIGN

Robots need to understand individual objects, not just detect them. MONAD bridges detection and manipulation with persistent segmentation and tracking. Click once — SAM initializes. XMem propagates. Persistent object identity across 30+ frames, feeding grasping, navigation, and annotation workflows.

MODULE STATUS: ACTIVE

Tracking Persistence

30+

DIVISION
ANIMA
WAVE
W1
DOMAIN
UNDERSTANDING
WAVE 1 // ANIMA SUITE
SEGMENTATION & TRACKING
MONAD // W1 // 049/079
01THE CHALLENGE

OBJECTS NEED IDENTITY, NOT JUST DETECTION

Robots need to understand individual objects, not just detect them. One button is different from another. But interactive segmentation today is one-shot: click, get a mask, then what? If the object moves, you're starting over.

Manufacturing, pick-and-place, and manipulation demand persistent object identity. You click once at the start of a task, and the system should follow that specific object through the rest of the scene, accounting for motion, occlusion, and lighting changes. MONAD solves this.

02THE SOLUTION

WHAT MONAD DELIVERS

MONAD is the productized segmentation and tracking system that bridges detection and manipulation. It takes a point or box prompt and returns persistent object masks.

CAPABILITIES

  • Interactive segmentation: initialize from point clicks, box guidance, or detector outputs
  • Persistent tracking: same object maintains stable ID across 30+ frames
  • SAM + XMem pipeline: paper-faithful implementation derived from IST-ROS research
  • Dual backends: demo for deterministic testing, paper (sam_xmem) for production
  • REST API endpoints: /segment, /track/init, /track/step, /track/status, /config, /health
  • MLX Apple Silicon (M1–M5, ~200ms on M5, 4× M1) + CUDA + CPU support
  • Docker profiles: CPU, GPU, Metal — production-ready deployment
03ENGINEERING

WHY THIS IS HARD

Segmentation-only systems don't track. Pure trackers don't segment. Combining them requires careful initialization and frame-to-frame consistency:

  1. 01Mask propagation drifts over time — XMem alone doesn't initialize from zero, you need SAM for that
  2. 02Frame-to-frame consistency demands temporal memory management with bounded buffers
  3. 03Multiple objects require collision-free mask propagation without ID switching
  4. 04Production inference paths differ from research notebooks — need deterministic validation
  5. 05Real-time performance requires careful device abstraction across MLX, CUDA, and CPU

MONAD ships both a research path (sam_xmem, paper-faithful) and a deterministic demo backend. It bundles model downloader, health checks, baseline runner, and structured outputs.

04BENCHMARKS

REAL HARDWARE PERFORMANCE

Measured with SAM + XMem pipeline:

REAL HARDWARE PERFORMANCE
METRICVALUE
Tracking Persistence30+ frames continuous
SAM Initialization~200ms (M5), ~50ms (RTX 4090)
XMem Propagation~80ms/frame (M5), ~15ms/frame (RTX 4090)
Apple M5 (MLX)~200ms init, ~80ms/frame (4× M1)
RTX 4090 (CUDA)~50ms init, ~15ms/frame
API Endpoints7 REST endpoints
Docker ProfilesCPU, GPU, Metal
05BUILD STATUS

WHAT'S BUILT TODAY

7/8 COMPONENTS COMPLETE
WHAT'S BUILT TODAY
COMPONENTSTATUSNOTES
Core modelsCOMPLETESAM + XMem pipeline, production-ready weights
API layerIN PROGRESSPublic API, pending dataset infrastructure
Demo BackendCOMPLETEDeterministic, locally testable
Paper Backend (sam_xmem)COMPLETEIntegrated, target-hardware ready
REST APICOMPLETE/segment, /track/init, /track/step, /track/status
Run RegistryCOMPLETEOverlay video, masks, metrics JSON
Quality GatesCOMPLETEruff, pytest, mypy passing
Docker ProfilesCOMPLETECPU, GPU, Metal production profiles
06APPLICATIONS

WHERE MONAD DEPLOYS

  • APP_01

    ROBOT GRASPING

    One-click object initialization into stable tracking for pick-and-place.

  • APP_02

    ASSEMBLY AUTOMATION

    Track individual parts through assembly steps without ID swaps.

  • APP_03

    HUMAN-IN-THE-LOOP ANNOTATION

    Operators mark objects once, system tracks them for labeling.

  • APP_04

    WAREHOUSE AUTOMATION

    Persistent bin and pallet tracking across camera views.

  • APP_05

    MANIPULATION RESEARCH

    Test segment-and-track hypotheses without reimplementing inference.

  • APP_06

    NAVIGATION

    Persistent understanding of obstacles through the scene.

07TECHNOLOGY

UNDER THE HOOD

FOUNDATION: IST-ROS

  • IST-ROS flexible segmentation framework (SoftwareX 2025)
  • SAM for interactive initialization + XMem for video object tracking
  • Paper-faithful implementation with production inference paths
  • Deterministic core suitable for safety-critical systems

MONAD IMPLEMENTATION

  • Python runtime with MLX (Apple Silicon) and CUDA support
  • Dual backend: demo (deterministic) + sam_xmem (production)
  • Health checks and readiness probes
  • Run registry with structured artifact output

INTEGRATION POINTS

  • Feeds object masks to manipulation planning modules
  • Provides persistent tracking to DAEMON (trajectory observation)
  • Outputs segmentation artifacts for downstream annotation (LOGOS)
08PAPERS

RESEARCH BASIS

  1. [01]IST-ROS: A flexible object segmentation and tracking framework for robotics — SoftwareX 2025