📚 Cognitive Synergy Architecture (CSA)

✨ Project Summary

CSA (Cognitive Synergy Architecture) is an advanced ROS 2–native framework for human-centric robots.
It integrates perception (SEGO), planning (IMAGO), reasoning (ONN/LOGOS) and meta-reasoning (D-LOGOS)
for robots that understand, reason, adapt plans, and explain decisions in real time.

CSA is not just code — it is a research journey that embodies my vision:

Robots that reason, reflect, and co-think with humans.

This repository contains the codebase, architecture, and demo pipelines for CSA research and development.

Modular cognition for robots that perceive, reason, and co-think with humans.

CSA is a multistage robotics framework that unifies semantic perception, adaptive planning, and explainable control under a single, ROS 2–native codebase. It is organized into three tightly-coupled sub-architectures SEGO, IMAGO, and LOGOS that together deliver a full perception-to-action loop with built-in reasoning, safety, and self-reflection.

Sub-Architecture	Core Question	Key Capabilities (current state)
SEGO (Semantic Graph Ontology mapper)	“What exactly am I seeing?”	• Real-time RGB-D detection (YOLO v5) • Multi-object tracking (StrongSORT) • 6-DoF pose fusion (ORB-SLAM2 / RTAB-Map) • 3-D semantic mapping + scene-graph export
IMAGO (Intent Modeling & Action Generation Operator)	“Why should I act and how do I adapt my plan?”	• Natural-language intent parsing (LLM plugin) • Symbolic & neuro-symbolic planners (HTN, BT, CoT) • PPO-based self-adaptation + meta-controller
LOGOS (Logical Ontological Generator for Self-adjustment)	“Does my behaviour remain safe, valid, and explainable?”	• Run-time ontology checks (OWL 2, DL-query) • Policy distillation → decision trees • Human-readable rationales (XAI report)

Status – 2025-06 14 SEGO Stage-1 is feature-complete; IMAGO & LOGOS scaffolding is included but most PRs are still WIP.
Road-mapped milestones are listed here ».

The CSA Stages

Stage	Module	Purpose
①	SEGO	Semantic mapping, scene graph construction
②	IMAGO	Intent-driven planning, adaptive control
③	ONN	Ontology Neural Network for topological reasoning
④	LOGOS	Explainable decision making, reasoning trace
⑤	D-LOGOS	Meta-reasoning, self-reflective rule evolution

🔬 Mathematical Foundations

Topological stability guarantee:

$$ d_{\rm PH}(G_C(t), G_C(t+\delta)) \le C_1 \sqrt{\mathcal{L}{\rm ricci}} + C_2 \mathcal{L}{\rm ph} $$

Phase margin under delay:

$$ \phi_{\rm margin}^{\rm effective} = \phi_{\rm design} - 360 f_c \Delta t + \phi_{\rm comp} \ge \phi_{\rm safe} $$

Adaptive control law:

$$ \dot{K} = -\alpha e(t) \dot{e}(t) $$ $$ V = \frac{1}{2} e^T P e $$ $$ \dot{V} = -e^T Q e \le 0 $$

📘 Learn More

This repository is based on the system architecture and cognitive vision proposed in
my own review papers:

These papers present the conceptual and theoretical foundation for CSA (Cognitive Synergy Architecture) —
a modular framework that integrates semantic perception (SEGO), intention-aware planning (IMAGO),
and ontological reasoning with explainable control (LOGOS).

✨ This project aims to realize the vision outlined in these papers:
to build robots that not only act, but also understand —
semantically, ethically, and reflectively, in collaboration with humans.

🚀 Quick Start

# 0 · Host prerequisites (Ubuntu 22.04 + ROS 2 Humble assumed)
sudo apt update && sudo apt install build-essential git lsb-release curl

# 1 · Clone
git clone https://github.com/jack0682/CSA.git
cd CSA && git submodule update --init --recursive

# 2 · Setup ROS 2 & Python env
source /opt/ros/humble/setup.bash        # adjust if using another distro
python3 -m venv .venv && source .venv/bin/activate
pip install -U pip wheel
rosdep install --from-paths src -yi      # system deps

# 3 · Build (SEGO only)
colcon build --packages-select \
    csa_interfaces csa_yolo_inference csa_slam_interface csa_semantic_mapper
source install/setup.bash

# 4 · Run semantic mapping pipeline
ros2 launch csa_launch sego_pipeline.launch.py \
    camera_model:=realsense bag:=false visualize:=true

SEGO System Pipeline

IMAGO System Pipeline

🧭 High-Level Architecture

graph TD

%% === SEGO: Semantic Mapping ===
subgraph SEGO [Stage 1: Semantic Mapping]
  YT[yolo_tracker_node] --> OBJS[/tracked_objects/]
  SLAM[slam_pose_node] --> CAM[/camera/pose/]
  OBJS --> MAP[semantic_mapper_node]
  CAM --> MAP
  MAP --> SG[Scene Graph - JSON]
  SG --> MEM[Semantic Memory]
end

%% === IMAGO: Intent Parsing + Planning ===
subgraph IMAGO [Stage 2: Intent Planning]
  INTENT[Intent Parser] --> GGRAPH[Goal Graph]
  MEM --> GGRAPH
  GGRAPH --> PLAN[Trajectory Planner]
end

%% === LOGOS: Reasoning + Control ===
subgraph LOGOS [Stage 3: Ontological Control]
  PLAN --> ACT[ϕ Actuator Commands]
  PLAN --> XAI[Ψ XAI Rationale]
  XAI --> HRI[HRI UI / Speech]
  ONT[Ω Ontology Rules] -.-> PLAN
end

classDef dim fill:#f9f9f9,stroke:#bbb,stroke-dasharray:5 5,color:#777;
class ONT dim;

Data Loop: sensor → semantic map → reasoning → plan → control → explanation

Safety Loop: LOGOS continuously validates each plan slice (Δt) via OWL-based rules; if violation, a fallback BT and human prompt are triggered.

🔬 Module Breakdown (SEGO Stage-1)

1 · csa_yolo_inference

Feature	Implementation
Detector	YOLO v5 6.2 + Ultralytics API
Tracker	StrongSORT (boxmot fork)
Depth Sampling	RealSense D435 / any aligned depth topic
Output Message	`csa_interfaces/TrackedObjectArray`

2 · csa_slam_interface

Wraps ORB-SLAM2 (stereo/RGB-D) or RTAB-Map. Publishes geometry_msgs/PoseStamped @ ≈30 Hz on /camera/pose. Automatic time-sync with YOLO frames via TF + approx-time-policy.

3 · csa_semantic_mapper

Projects 2-D bounding boxes into 3-D world frame: Builds an online scene graph (network-x) and dumps incremental .json logs per track (ready for Neo4J ingestion).

{
  "class"   : "cbn_wooden_box",
  "track_id": 27,
  "position": [ 0.773, -0.142, 1.035 ],
  "size"    : [ 0.28, 0.35, 0.12 ],     // meters
  "pose_cov": [ 5.2e-4, 8.9e-4, … ],
  "timestamp": 1715530123.447,
  "world_frame": "map",
  "source": {
    "camera_frame": "realsense_link",
    "depth_px": 713
  }
}

4 · Visualization

Launch RViz2 with pre-loaded config: /rviz/sego_live.rviz Display: TF tree, /tracked_objects markers (ID-color keyed), occupancy map.

🖥️ Installation & Build

Layer	Package	Version	Install hint
System	`Ubuntu`	22.04 LTS	`apt …`
ROS 2	`Humble Hawksbill`	≥ 0.11	link
Vision	`OpenCV`	4.9	`apt install libopencv-dev`
ML	`PyTorch`	2.2 + CUDA-11.7	`pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117`
SLAM	`ORB-SLAM2`	custom	included as git submodule
Tracker	`boxmot`	0.2.x	`pip install boxmot`
Misc	`pangolin` / `eigen3`	latest	`apt install libeigen3-dev libgl1-mesa-dev`

🎮 Runtime Flow

Step	Node	Topic	Rate	Note
①	Sensor	`/camera/color/image_raw`	30 Hz	RealSense RGB
②	`yolo_tracker_node`	`/tracked_objects`	15 Hz	after NMS + tracker
③	`slam_pose_node`	`/camera/pose`	30 Hz	world–camera TF
④	`semantic_mapper_node`	`/scene_graph` (latched)	2 Hz	heavy but sparse
⑤	`rviz2`	markers	live	for debugging

📂 Repository Layout

CSA/
├── docs/                 ← diagrams, papers, design notes
├── src/
│   ├── csa_interfaces/   ← ROS 2 msg definitions
│   ├── csa_yolo_inference/
│   ├── csa_slam_interface/
│   ├── csa_semantic_mapper/
│   ├── csa_utils/        ← time_sync, coord transforms
│   ├── csa_launch/
│   ├── imago_core/       ← planner skeleton (Stage 2)
│   └── logos_core/       ← ontology + XAI (Stage 3)
├── third_party/          ← submodules (ORB-SLAM2, pangolin…)
├── docker/
├── .devcontainer/        ← VS Code remote config
└── README.md             ← you are here

🗺️ Roadmap & Milestones

Quarter	Theme	Deliverable
2025 Q3	IMAGO α	HTN planner; LLM intent parser
2025 Q4	LOGOS α	Live policy distillation + OWL safety layer
2026 Q1	Multi-robot demo	Cross-agent shared scene graphs
2026 Q2	HRI pilot	Common-ground intent classifier, voice+gesture
2026 Q4	Science Robotics submission	End-to-end human+robots furniture assembly

IMAGO Control Architecture Backbone

⚙️ IMAGO + Panda Integrated Design

⚙️ IMAGO Core Structure

High-Level Input

TEXT QUERY
Parses user language or natural language commands into intent representations.
Connected with an sLLM-based text query and answering system.
SEGO MODULE
Provides semantic information via scene graphs.
Utilizes SEGO Scene Graph and Ontology Database.

Control Layer

Adaptive Compensator (Pole-Zero Autonomous)
Real-time error correction based on pole-zero compensation.
Receives rule/state feedback from the LOGOS MODULE for compensator adjustment.
Self-Adjusting Controller
Adaptive control optimization.
Real-Time Motion Executor
Executes final motion commands at the physical level.

LOGOS Interaction

Adaptive Compensator & LOGOS MODULE:
Pole-zero compensation rule exchange
Real-time rule optimization
Meta-level reasoning feedback
Handshake with real-time executor

⚙️ Robot Arm Requirements

✅ Provides joint-level control interface (Torque / Velocity / Position)
✅ Real-time control loop period <1ms (RTOS or RT-capable)
✅ Modifiable controller (Open Source or SDK provided)
✅ Access to high-performance sensors/drivers (Force-Torque, Encoders)
✅ Easy ROS2 or RTAPI integration
✅ Low-level bus access (e.g., EtherCAT, CANopen)
✅ Public SDK / HAL available

👉 Target robot arm: Franka Emika Panda

⏱ Delay Issues and Architectural Limitations

libfranka API provides access above the HAL wrapping layer.
Torque commands are injected after internal impedance safety layers.
Latency accumulates through ROS2 → API → internal RT thread → driver stack.
Estimated accumulated delay: 3–5 ms, which threatens phase margin and may induce oscillations.

📐 IMAGO Control Formulation

Plant Model

$$ G(s) = \frac{1}{J s^2 + B s} $$

Pole-Zero Compensator Design

Ideal design target:

$$ C(s) G(s) = 1 $$

Actual compensator:

$$ C(s) = J' s^2 + B' s $$

Delay-Aware Controller

With delay:

$$ G_d(s) = G(s) e^{-s \Delta t} $$

Effective compensator:

$$ C(s) G_d(s) = C(s) G(s) e^{-s \Delta t} $$

Phase margin degradation:

$$ \Phi_{\text{margin,eff}} = \Phi_{\text{design}} - 360 f \Delta t $$

Delay Compensation

Lead compensator form:

$$ C_{\text{delay}}(s) = \frac{\alpha T_{\text{lead}} s + 1}{T_{\text{lead}} s + 1}, \quad 0 < \alpha < 1 $$

Smith predictor (model-based):

$$ \tau_{\text{cmd}}(s) = C(s) \frac{G(s)}{G_d(s)} r(s) $$

Final torque command:

$$ \tau_{\text{cmd}}(s) = \left( J' s^2 + B' s \right) C_{\text{delay}}(s) e^{+s \Delta t} r(s) $$

Wrapping Transfer Function

$$ \tau_{\text{actual}}(s) = H_{\text{int}}(s) C(s) C_{\text{delay}}(s) e^{+s \Delta t} r(s) $$

Where:

$$ H_{\text{int}}(s) = \frac{\theta(s)}{\tau(s)} = \frac{1}{M s^2 + B s} $$

Nonlinear MIMO Dynamics

$$ M(\theta) \ddot{\theta} + C(\theta, \dot{\theta}) \dot{\theta} + G(\theta) = \tau_{\text{actual}} $$

$$ M(\theta) = \sum_{i=1}^6 M_i(\theta_1, \dots, \theta_i) $$

$$ C(\theta, \dot{\theta}) = \sum_{i=1}^6 \sum_{j=1}^6 c_{ij}(\theta) \dot{\theta}_j $$

$$ c_{ij}(\theta) = \frac{1}{2} \left[ \frac{\partial M_{ij}}{\partial \theta_j} + \frac{\partial M_{ij}}{\partial \theta_i} + \frac{\partial M_{jj}}{\partial \theta_i} \right] $$

$$ G(\theta) = \sum_{i=1}^6 g_i(\theta) $$

🔧 Self-Adjusting Adaptive Control Law

Adaptive update:

$$ \dot{K} = -\alpha e(t) \dot{e}(t) $$

Lyapunov function:

$$ V = \frac{1}{2} e^\top P e, \quad \dot{V} = -e^\top Q e \leq 0 $$

With delay compensation:

$$ e_J(t - \Delta t), \quad e_B(t - \Delta t) $$

⚙️ Multi-Robot End-Effector Plan Synchronization

Global plan reference:

$$ \chi_d^{\text{ref}}(t) $$

Synchronization constraint:

$$ \forall i,j: \left| \chi_d^{\text{ee,i}}(t) - \chi_d^{\text{ref}}(t) \right| < \varepsilon_{\text{sync},ij} \quad \wedge \quad \left| \Delta t^i - \Delta t^j \right| < \varepsilon_{\text{latency}} $$

Synchronization torque:

$$ \tau_{\text{actual}}^i(s) \leftarrow \tau_{\text{actual}}^i(s) + K_{\text{sync}} \sum_j \left( \chi_d^{\text{ee,j}}(t) - \chi_d^{\text{ee,i}}(t) \right) $$

⚙️ Final IMAGO Wrapping Control Structure

$$ \tau_{\text{actual}}(s) = H_{\text{int}}(s) C^{\text{ML}}(s) C_{\text{delay}}(s) e^{+s \Delta t} \mathcal{L} \theta_d^{\text{final}}(t) $$

Notes

Franka internal controller:
RTOS + safety layers + motor driver layers → user control applies above HAL.
Internal safety core modification is not allowed.
libfranka loop:
1 kHz control loop → 1 ms period
ROS2 → C++ API call → internal RT thread
Delay accumulation risk:
Accumulated delay of several ms reduces phase margin, induces oscillation or overshoot.

Potential Extended Formulations

Multi-joint Smith predictor:

$$ \tau_{\text{cmd}}(s) = \sum_{i=1}^6 C_i(s) \frac{G_i(s)}{G_{d,i}(s)} r_i(s) $$

Frequency response with delay:

$$ |C(j\omega) G(j\omega)| \angle e^{-j \omega \Delta t} $$

Phase margin with frequency dependency:

$$ \Phi_{\text{margin,eff}}(\omega) = \Phi_{\text{design}}(\omega) - 360 \frac{\omega}{2 \pi} \Delta t $$

imago_controller/
├── CMakeLists.txt                  # ROS2 + C++ 빌드 파일
├── package.xml                      # ROS2 패키지 메타 정보
├── launch/
│   └── imago_controller.launch.py   # ROS2 launch 파일
├── config/
│   ├── controller_params.yaml       # IMAGO 파라미터 (J, B, delay 등)
│   └── sync_params.yaml             # 다중 로봇 sync 파라미터
├── include/
│   └── imago_controller/
│       ├── imago_controller.hpp          # 메인 클래스 선언
│       ├── pole_zero_compensator.hpp     # Pole-Zero 보상기 클래스
│       ├── delay_compensator.hpp         # Delay-aware 보상기 클래스
│       ├── ml_estimator.hpp              # ML 기반 inertia/Coriolis/gravity 추정
│       ├── sync_manager.hpp              # 다중 로봇 sync 관리
│       ├── low_level_interface.hpp       # libfranka / EtherCAT 인터페이스
│       └── utils.hpp                     # 공통 함수 (matrix ops, logging 등)
├── src/
│   ├── imago_controller.cpp              # 메인 control loop
│   ├── pole_zero_compensator.cpp
│   ├── delay_compensator.cpp
│   ├── ml_estimator.cpp
│   ├── sync_manager.cpp
│   ├── low_level_interface.cpp
│   └── main.cpp                          # ROS2 노드 진입점
├── urdf/
│   └── panda_with_imago.urdf.xacro       # Panda + IMAGO 추가 링크/센서 URDF
├── meshes/
│   └── (필요 시 custom link/EE mesh 파일)
├── scripts/
│   └── test_plot.py                      # Latency/torque/sync 데이터 시각화
├── tests/
│   └── test_imago_controller.cpp         # gtest 기반 유닛테스트
├── docs/
│   └── design_overview.md                # 설계 문서
├── data_logs/
│   └── (실험 중 생성되는 log 파일 저장)
└── README.md                             # 전체 개요 설명

Name		Name	Last commit message	Last commit date
Latest commit History 62 Commits
docs/media		docs/media
src		src
third_party		third_party
.gitignore		.gitignore
IMAGOPanda_design_Draft.png		IMAGOPanda_design_Draft.png
IMAGO_System_pipline.png		IMAGO_System_pipline.png
README.md		README.md
SEGO_System_pipline.png		SEGO_System_pipline.png

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📚 Cognitive Synergy Architecture (CSA)

✨ Project Summary

This repository contains the codebase, architecture, and demo pipelines for CSA research and development.

The CSA Stages

🔬 Mathematical Foundations

📘 Learn More

📑 Table of Contents

🚀 Quick Start

SEGO System Pipeline

IMAGO System Pipeline

🧭 High-Level Architecture

🔬 Module Breakdown (SEGO Stage-1)

1 · csa_yolo_inference

2 · csa_slam_interface

3 · csa_semantic_mapper

4 · Visualization

🖥️ Installation & Build

🎮 Runtime Flow

📂 Repository Layout

🗺️ Roadmap & Milestones

IMAGO Control Architecture Backbone

⚙️ IMAGO + Panda Integrated Design

⚙️ IMAGO Core Structure

High-Level Input

Control Layer

LOGOS Interaction

⚙️ Robot Arm Requirements

⏱ Delay Issues and Architectural Limitations

📐 IMAGO Control Formulation

Plant Model

Pole-Zero Compensator Design

Delay-Aware Controller

Delay Compensation

Wrapping Transfer Function

Nonlinear MIMO Dynamics

🔧 Self-Adjusting Adaptive Control Law

⚙️ Multi-Robot End-Effector Plan Synchronization

⚙️ Final IMAGO Wrapping Control Structure

Notes

Potential Extended Formulations

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