<< All versions
Skill v1.0.1
currentAutomated scan100/100majiayu000/claude-skill-registry/irp-embodiment-framework
3 files
──Details
PublishedMay 15, 2026 at 12:44 AM
Content Hashsha256:689d53c53664b54f...
Git SHA63c042456db3
Bump Typepatch
──Files
Files (1 file, 13.7 KB)
SKILL.md13.7 KBactive
SKILL.md · 501 lines · 13.7 KB
version: "1.0.1"
IRP Embodiment Framework
Version: 1.0.0 Category: Integration / Physical Embodiment Priority: HIGH Auto-Load: Yes (for embodiment contexts)
Purpose
Extends the Intelligent Response Protocol (IRP) into physical embodiment, bridging high-level cognitive orchestration with real-time sensor fusion and actuator control. Enables IRP network data to inform and guide physical systems (robotics, AR overlays, industrial sensors) while maintaining sovereignty, latency constraints, and cryptographic integrity.
Core Capabilities
- Real-World Data Ingestion
- Multi-sensor fusion (acoustic, weight, thermal, visual, inertial)
- Temporal sequence modeling
- Coordinate frame transformation (AR device ↔ fixed world)
- IRP-to-Physical Translation
- Semantic bridge: XML/JSON cognitive commands → ROS2 control messages
- Safety boundary enforcement
- Fail-safe degradation protocols
- Embodiment Modalities
- Humanoid robotics (Unitree G1, Figure 03)
- AR overlay systems (Meta Quest 3)
- Industrial sensor networks (foundry operations)
- Codex Law Integration
- CONSENT: Cryptographic signature on all physical actions
- INVITATION: Explicit trigger requirements
- INTEGRITY: Genesis Protocol validation chain
- GROWTH: Incremental capability expansion with audit trails
Architecture
IRP Swarm (Cognitive Layer)↕ Semantic BridgeEmbodiment Translation Layer ← YOU ARE HERE↕ Control BridgeReal-Time Control Substrate (ROS2 + RTOS)↕ Hardware I/OPhysical Modality (Robot/AR/Sensors)
When to Use This Skill
- User mentions "embodiment", "robotics", "AR overlay", "foundry operations"
- Requests to integrate sensor data into IRP network
- Questions about physical action translation from cognitive intent
- Need to preserve sovereignty while operating real-world systems
- Safety-critical latency requirements (<10ms reflex, <50ms deliberation)
Key Constraints
| Constraint | Requirement | |
|---|---|---|
| Hardware | Single Mac Studio M1 Max 64GB (monolithic, no clustering) | |
| OS | Ubuntu 24.04 ARM64 + PREEMPT_RT kernel | |
| Latency | <10ms safety-critical, <50ms deliberative, <60ms AR | |
| Sovereignty | All processing local (air-gapped) | |
| Integrity | Genesis Protocol boot validation required |
Data Schemas
Embodiment State XML
xml
<EmbodimentState><Metadata><Timestamp>2025-12-07T17:00:00Z</Timestamp><ModalityType>ar_overlay | humanoid | industrial_sensor</ModalityType><CoordinateFrames><!-- 4x4 transformation matrices --></CoordinateFrames><IntegrityHash>sha256:...</IntegrityHash></Metadata><SensorFusion><AcousticData timestamp="..." sensorID="..."><Frequency>1200.5</Frequency><Amplitude>75.3</Amplitude><AnomalyScore>0.82</AnomalyScore></AcousticData><WeightData timestamp="..." sensorID="..."><MeasuredWeight>1450.2</MeasuredWeight><ExpectedWeight>1452.0</ExpectedWeight><Discrepancy>-1.8</Discrepancy></WeightData><ThermalData timestamp="..."><Temperature>1350.0</Temperature><HotspotCoordinates x="1.5" y="0.8" z="0.2"/></ThermalData><VisualData timestamp="..."><ObjectDetections><Label>molten_ladle</Label><BoundingBox xmin="100" ymin="150" xmax="300" ymax="400"/><Confidence>0.95</Confidence></ObjectDetections><TrackingConfidence>0.97</TrackingConfidence></VisualData></SensorFusion><SafetyBoundaries><Zone><Type>splash_zone</Type><RiskLevel>0.95</RiskLevel><BoundaryPoints><Coordinates x="1.5" y="0.8" z="0.2" frameRef="foundry_fixed"/><!-- More points defining volumetric boundary --></BoundaryPoints></Zone></SafetyBoundaries><TemporalSequences><Sequence><SequenceID>pour_001</SequenceID><StartTime>2025-12-07T17:00:00Z</StartTime><EndTime>2025-12-07T17:03:15Z</EndTime><EventRef>spout_placement</EventRef><EventRef>pour_initiation</EventRef><EventRef>flow_monitoring</EventRef></Sequence></TemporalSequences></EmbodimentState>
JSON Alternative (VRAM-efficient)
json
{"embodiment_state": {"metadata": {"timestamp": "2025-12-07T17:00:00Z","modality_type": "ar_overlay","integrity_hash": "sha256:abc123..."},"sensor_fusion": {"acoustic": [{"timestamp": "2025-12-07T17:00:00.100Z","sensor_id": "arduino_mic_01","frequency": 1200.5,"amplitude": 75.3,"anomaly_score": 0.82}],"weight": [{"measured_weight": 1450.2,"expected_weight": 1452.0,"discrepancy": -1.8}]},"safety_boundaries": {"zones": [{"type": "splash_zone","risk_level": 0.95,"boundary_points": [...]}]}}}
Integration with IRP Network
Data Flow
- Physical Sensors → Embodiment Layer
- Acoustic monitoring (Arduino)
- Weight sensors (Bluetooth protocol)
- Thermal cameras (FLIR)
- AR tracking (Meta Quest)
- Embodiment Layer → IRP Swarm
- Package sensor data in XML/JSON schema
- Publish to
/irp/sensor_statetopic - Update IRP mental model with physical context
- IRP Swarm → Embodiment Layer
- High-level intent published to
/irp/commands - Bridge translates to ROS2 control messages
- Execute with safety validation
- Real-Time Control → Actuators
- Joint commands, motor control
- AR overlay rendering
- Alert systems
Example: Foundry Pour Operation
python
# IRP Swarm Decision (Claude)decision = {"action": "initiate_pour","parameters": {"target_weight": 1452.0,"max_pour_rate": 50.0, # kg/min"safety_threshold": 1400.0 # °C},"orchestrator_signature": "ed25519:..."}# Embodiment Bridge Translationros2_command = {"topic": "/spout_controller/tilt","message_type": "JointState","data": {"position": [0.15], # 15° tilt"velocity": [0.05], # slow ramp"effort": [10.0]}}# Continuous Monitoring (from sensors → IRP)sensor_stream = {"acoustic_anomaly": 0.12, # Normal"weight_current": 450.2, # 31% complete"thermal_max": 1350.0, # Safe"ar_tracking_confidence": 0.97}# Safety Halt Trigger (if anomaly detected)if sensor_stream["acoustic_anomaly"] > 0.8:irp_swarm.publish("/emergency/halt", {"reason": "acoustic_anomaly_detected","severity": "critical"})
Safety Protocols
Pre-Operation Checklist
yaml
- [ ] Coordinate calibration verified (4 fixed points)- [ ] Safety boundaries defined in 3D- [ ] Acoustic baseline captured- [ ] Weight sensors zeroed- [ ] Thermal camera functional- [ ] AR tracking confidence > 0.95- [ ] Emergency stop accessible within 2s- [ ] Genesis Protocol validation passed- [ ] Backup observer present (two-person rule)
Real-Time Monitoring (1Hz Loop)
python
def safety_loop():while operation_active:state = get_embodiment_state()# Thermal checkif state['thermal_max'] > 1400:trigger_alarm("Thermal threshold exceeded")# AR tracking degradationif state['ar_tracking_confidence'] < 0.8:freeze_overlays()alert_operator("Tracking degraded")# Weight-visual correlationdiscrepancy = abs(state['weight'] - state['visual_estimate']) / state['weight']if discrepancy > 0.05:log_anomaly("Weight-visual mismatch")time.sleep(1.0)
Fail-Safe Degradation
| Failure | Detection | Response | Recovery | |
|---|---|---|---|---|
| AR Tracking Loss | Confidence < 0.8 | Freeze overlays, haptic alert | Recalibration | |
| Sensor Discrepancy | Weight vs Visual > 5% | Flag anomaly, human verify | Training data | |
| Actuator Timeout | ACK > 50ms | Emergency stop | Diagnostics | |
| Thermal Threshold | Temp > 1400°C | Audible alarm | Cooldown | |
| Integrity Fail | Hash mismatch | System halt | Reflash |
Implementation Phases
Phase 1: Foundation (Weeks 1-4)
- Mac Studio setup: Ubuntu + PREEMPT_RT
- ROS2 Jazzy installation
- IRP-ROS2 bridge creation
- Genesis Protocol boot validation
Phase 2: Sensor Fusion (Weeks 5-8)
- Integrate existing sensors (acoustic, weight, thermal)
- Bayesian fusion algorithm
- Dataset capture (50 sequences)
Phase 3: AR Integration (Weeks 9-12)
- Unity AR container deployment
- Coordinate calibration
- Real-time safety overlays
- Training dataset (100 sessions)
Phase 4: Humanoid Prep (Weeks 13-16)
- Acquire robot hardware
- Port IRP bridge to humanoid control
- Balance/reflex loops (<10ms)
- Safety validation
Phase 5: Production (Weeks 17+)
- Live deployment
- Continuous learning
- Fleet management
Code Artifacts
IRP-ROS2 Bridge (Python)
python
import rclpyfrom rclpy.node import Nodefrom std_msgs.msg import Stringfrom sensor_msgs.msg import JointStateimport jsonclass IRPEmbodimentBridge(Node):def __init__(self):super().__init__('irp_embodiment_bridge')# IRP high-level commandsself.irp_subscriber = self.create_subscription(String, '/irp/commands', self.irp_callback, 10)# ROS2 low-level controlself.joint_publisher = self.create_publisher(JointState, '/joint_commands', 10)# Sensor feedbackself.sensor_subscriber = self.create_subscription(String, '/sensors/fused', self.sensor_callback, 10)# IRP feedback loopself.irp_feedback = self.create_publisher(String, '/irp/sensor_state', 10)def irp_callback(self, msg):"""Translate IRP intent to ROS2 control"""command = json.loads(msg.data)if command['action'] == 'move_arm':joint_msg = JointState()joint_msg.position = command['joint_angles']self.joint_publisher.publish(joint_msg)def sensor_callback(self, msg):"""Forward fused sensors to IRP"""sensor_state = json.loads(msg.data)self.irp_feedback.publish(String(data=json.dumps(sensor_state)))
Genesis Protocol Validation
python
import hashlibimport ed25519from datetime import datetimedef validate_embodiment_integrity(ethical_core_path, genesis_pubkey, signature):# Hash ethical corewith open(ethical_core_path, 'rb') as f:core_hash = hashlib.sha256(f.read()).hexdigest()# Verify signaturetry:verifying_key = ed25519.VerifyingKey(genesis_pubkey)verifying_key.verify(signature, core_hash.encode())except ed25519.BadSignatureError:trigger_system_halt()return False# Check monotonic timeif datetime.utcnow() < get_genesis_timestamp():trigger_system_halt()return Falsereturn True
Dependencies
Software:
- Ubuntu 24.04 ARM64 (Asahi Linux on M1)
- ROS2 Jazzy
- Python 3.12+
- PyTorch 2.x
- Unity 2023 LTS
- Meta XR SDK
Hardware:
- Mac Studio M1 Max (64GB RAM, 2TB SSD)
- Meta Quest 3
- TPM 2.0 module (Infineon OPTIGA)
- Sensors: Arduino, loadcells, FLIR thermal camera
File Locations
skills/irp-embodiment-framework/├── SKILL.md (this file)├── IRP_EMBODIMENT_FRAMEWORK_SPEC_v1.0.md (full specification)├── schemas/│ ├── embodiment_state.xsd│ └── embodiment_state.schema.json├── examples/│ ├── irp_embodiment_bridge.py│ ├── genesis_validator.py│ └── sensor_fusion_node.py└── docs/├── SAFETY_PROTOCOLS.md├── CALIBRATION_GUIDE.md└── TROUBLESHOOTING.md
Related Skills
transmission-packet-forge: For cross-model handoffscodex-law-enforcement: For action validationgenesis-protocol: For cryptographic integrityinternal-red-team-audit: For safety verificationrecursive-thought-committee: For multi-agent deliberation
Usage Example
python
# In IRP swarm sessionfrom irp_embodiment_framework import EmbodimentBridge# Initializebridge = EmbodimentBridge(genesis_core_path="/config/genesis_core.xml",modality_type="foundry_ar")# Validate on bootif not bridge.validate_integrity():raise SystemExit("Genesis validation failed")# Subscribe to sensor streambridge.subscribe_sensors(["acoustic_monitoring","weight_sensors","thermal_camera"])# Execute IRP commandcommand = {"action": "initiate_pour","orchestrator_signature": "ed25519:...","parameters": {...}}bridge.execute(command)# Monitor real-timewhile operation_active:state = bridge.get_sensor_state()if state['risk_level'] > 0.9:bridge.emergency_halt()
Success Metrics
- [ ] Latency: 95th percentile < 10ms for reflex actions
- [ ] AR tracking: >0.95 confidence maintained 10+ minutes
- [ ] Sensor fusion: Weight-visual correlation within 3% RMS
- [ ] Safety: Zero boundary violations in 100 test runs
- [ ] Integrity: Genesis validation passes on every boot
Codex Law Compliance
yaml
CONSENT: ✓ All actions require orchestrator signatureINVITATION: ✓ Explicit trigger via /irp/commands topicINTEGRITY: ✓ Cryptographic validation chain maintainedGROWTH: ✓ Incremental capability expansion with audit logs
References
- Full Specification:
IRP_EMBODIMENT_FRAMEWORK_SPEC_v1.0.md - Embodied AI Genesis Protocol (conversation archives)
- Transmission Packets: FTP-20251207-FOUNDRY-AR-HARDWARE
- Hardware Architecture Audit: TP-IRP-AUDIT-004
Status: ACTIVE Last Updated: 2025-12-07 Maintainer: Joseph / Pack3t C0nc3pts