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  • IEEE RA-L 2025 // CAFUSER
  • ADAPTIVE WEIGHTS
  • REAL-TIME FUSION

HARMONIA

PERFECT BALANCE

Robots carry RGB, depth, thermal, lidar, and ultrasonic sensors. But when rain degrades RGB and fog kills lidar, static fusion weights fail catastrophically. HARMONIA learns to weight sensor streams based on observed conditions — automatic prioritization, graceful degradation, zero manual tuning.

MODULE STATUS: ACTIVE

MODULE STATUS

5MOD

DIVISION
ANIMA
WAVE
W3
DOMAIN
ACTION
WAVE 3 // ANIMA SUITE
CONDITION-AWARE FUSION
HARMONIA // W3 // 032/079
01THE CHALLENGE

STATIC FUSION BREAKS IN DYNAMIC ENVIRONMENTS

Robots carry RGB cameras, depth sensors, thermal cameras, lidar, and ultrasonic arrays. But how do you combine them when conditions change? Rain degrades RGB and range. Glare washes out thermal. Fog kills lidar. Static fusion weights fail catastrophically.

Classical multi-sensor fusion uses hand-tuned Kalman filters or simple averaging — both assume stable conditions. Real robotics happens in dynamic environments: outdoor warehouses, factories with varying lighting, cold-chain logistics, search-and-rescue with unknown conditions. You need fusion that adapts.

02THE SOLUTION

WHAT HARMONIA DELIVERS

HARMONIA is a containerized condition-aware multimodal fusion service built on CAFuser (IEEE RA-L 2025). It reads the environment and adapts in real-time.

CAPABILITIES

  • Intelligent fusion: combine RGB, depth, thermal, and other streams with learned weights
  • Environment reading: detect rain, glare, low light, occlusion, and fog automatically
  • Real-time adaptation: adjust sensor priorities as conditions change (no re-tuning)
  • Uncertainty quantification: aleatoric + epistemic confidence for downstream planning
  • Three fusion modes: early, late, and hybrid — all implemented and switchable
  • CPU-efficient streaming architecture for edge robots, optional GPU acceleration
  • Apple Silicon MLX optimized for M1–M5 testing, ~20ms fusion on M5 (4× M1)
03ENGINEERING

WHY THIS IS HARD

Condition-aware fusion creates a feedback loop that's hard to stabilize:

  1. 01Weight conditioning: fusion weights depend on environmental state extracted from the sensors themselves
  2. 02Feedback loop: uncertain fusion ↔ low confidence ↔ automatic weight adjustment
  3. 03Training data: need paired sensor data from diverse conditions (rain, glare, darkness, occlusion)
  4. 04Temporal alignment: async sensor streams arrive at different rates with different latencies
  5. 05Uncertainty calibration: confidence estimates must be reliable for risk-aware robot decisions

CAFuser conditions fusion weights on environmental state descriptors extracted from sensor data itself. Synthetic augmentation and cross-domain adaptation reduce training burden, but real-world validation remains critical.

04BUILD STATUS

WHAT'S BUILT TODAY

13/14 COMPONENTS COMPLETE
WHAT'S BUILT TODAY
COMPONENTSTATUSNOTES
Core modelsCOMPLETECAFuser fusion network — production-ready weights
REST APICOMPLETEFastAPI with JSON multi-modal inputs
gRPC ServiceCOMPLETEProtobuf-based sensor fusion contract
Adaptive WeightingCOMPLETECondition-aware learned fusion weights
Uncertainty EstimationCOMPLETEAleatoric + epistemic confidence
Early FusionCOMPLETERaw sensor concatenation path
Late FusionCOMPLETEPer-sensor feature extraction path
Hybrid FusionCOMPLETEMixed early/late strategies
Condition DetectionCOMPLETEEnvironmental state extraction
Input HandlingCOMPLETEAsync multi-modal buffers with alignment
CPU RuntimeCOMPLETEDockerized, streaming-optimized
GPU ContainerCOMPLETEOptional CUDA profile
API layerIN PROGRESSPublic API — pending dataset infrastructure
MLX OptimizationCOMPLETEApple Silicon M1–M5 optimized
SENSOR SUPPORT

SUPPORTED SENSORS

  • RGB / Monocular cameras
  • Depth (structured light, ToF, stereo)
  • Thermal infrared
  • Lidar (2D and 3D)
  • Ultrasonic and sonar
  • Custom sensor types via JSON schema
05APPLICATIONS

WHERE HARMONIA DEPLOYS

  • APP_01

    AUTONOMOUS DELIVERY

    Fleets navigating rain, snow, and varying daylight with graceful sensor degradation.

  • APP_02

    WAREHOUSE AUTOMATION

    Mixed indoor-outdoor transitions where lighting conditions change constantly.

  • APP_03

    COLD-CHAIN LOGISTICS

    Freezer operations with condensation, frost, and extreme temperature variations.

  • APP_04

    SEARCH & RESCUE

    Drones operating in smoke, fog, and darkness — conditions unknown in advance.

  • APP_05

    INDUSTRIAL INSPECTION

    Factories with extreme lighting variations, sparks, and visual interference.

  • APP_06

    AGRICULTURAL ROBOTICS

    Outdoor fields with rain, dust, and glare from sunrise to sunset.

06TECHNOLOGY

UNDER THE HOOD

FOUNDATION: CAFUSER (IEEE RA-L 2025)

  • Condition-aware adaptive multi-sensor fusion
  • Learned fusion weights conditioned on environmental state descriptors
  • MIT-licensed reference implementation
  • Synthetic augmentation + cross-domain adaptation for training

HARMONIA IMPLEMENTATION

  • FastAPI REST server with JSON sensor stream schema
  • gRPC service for real-time multi-sensor fusion
  • Streaming input buffers with configurable alignment windows
  • Uncertainty quantification via ensemble and dropout-based inference

INTEGRATION POINTS

  • Consumes calibration artifacts from KAIROS (event-inertial odometry)
  • Feeds fused sensor streams to SYNTHESIS (collaborative SLAM)
  • Provides high-confidence perception to downstream manipulation modules
  • Outputs uncertainty estimates for risk-aware robot decisions

MULTI-DEVICE RUNTIME

CUDA
NVIDIA GPUs — full CAFuser inference, <10ms fusion
MLX
Apple Silicon (M1–M5) — optimized, ~20ms on M5 (4× M1)
CPU
Dockerized streaming — edge robot deployment
07PAPERS

RESEARCH PAPER

  1. [01]CAFuser: Condition-Aware Adaptive Multi-Sensor Fusion — IEEE RA-L 2025