The rise of unmanned ``dark factories'' operating without human presence demands autonomous safety systems capable of detecting and responding to multiple hazard types. We present SafeGuard ASF (Agentic Security Fleet), a comprehensive framework deploying humanoid robots for autonomous hazard detection in industrial environments. Our system integrates multi-modal perception (RGB-D imaging), a ReAct-based agentic reasoning framework, and learned locomotion policies on the Unitree G1 humanoid platform. We address three critical hazard scenarios: fire and smoke detection, abnormal temperature monitoring in pipelines, and intruder detection in restricted zones. Our perception pipeline achieves 94.2% mAP for fire or smoke detection with 127ms latency. We train multiple locomotion policies, including dance motion tracking and velocity control, using Unitree RL Lab with PPO, demonstrating stable convergence within 80,000 training iterations. We validate our system in both simulation and real-world environments, demonstrating autonomous patrol, human detection with visual perception, and obstacle avoidance capabilities. The proposed ToolOrchestra action framework enables structured decision-making through perception, reasoning, and actuation tools.
Figures & tables
Fig. 1 : Six-layer hierarchical architecture of SafeGuard ASF. Sensor data flows through perception (L1) and understanding (L2) layers to inform decision-making (L4), which coordinates planning (L5) and action (L6) for hazard response.
Fig. 2 : Locomotion policy training pipeline using Unitree RL Lab. Training in Isaac Sim with 4096 parallel environments and domain randomization, followed by sim-to-sim validation in MuJoCo and sim-to-real deployment on the physical G1 robot.
Category
Count
Examples
Perception
8
fire_smoke, thermal_scan, depth_localize
Reasoning
5
fire_severity, thermal_hazard, spill_hazard
Knowledge
4
facility_map, thermal_baselines, personnel_db
Actuation
6
remote_valve, fire_suppression, locomotion
TABLE I : ToolOrchestra Tool Categories
Fig. 3 : Fire detection and response scenario. The robot detects fire via visual perception, assesses severity, coordinates suppression activation, and monitors until extinguishment. Timeline shows detection at T=0, alert at T+3s, suppression at T+18s, and confirmation at T+48s.
Hazard Type
Precision
Recall
F1
Latency
Fire
0.962
0.924
0.942
127ms
Smoke
0.918
0.891
0.904
127ms
Thermal Anomaly
0.895
0.936
0.915
83ms
Intruder
0.971
0.948
0.959
152ms
Average
0.937
0.925
0.930
122ms
TABLE II : Detection Performance by Hazard Type
Fig. 4 : Qualitative results of dance training.
Fig. 5 : Qualitative results of Gangnam training.
Fig. 6 : Qualitative results of velocity training.
Fig. 7 : Qualitative results of IsaacSim simulation
Phase
Fire
Thermal
Intruder
Detection
0.13
0.08
0.15
Reasoning
0.85
1.23
0.72
Alert Transmission
0.42
0.38
0.35
Navigation (avg)
8.50
6.20
5.80
Intervention
2.50
3.40
1.80
Total
12.40
11.29
8.82
TABLE III : Response Time Breakdown (seconds)
Scenario
Success
Partial
Failure
Fire/Smoke Response
92%
6%
2%
Thermal Anomaly
88%
8%
4%
Intruder Detection
88%
10%
2%
Overall
89.3%
8.0%
2.7%
TABLE IV : Scenario Success Rate (N=20 per scenario)
System
Coverage
Blind Spots
Response
Success
Rule-Based
100%
0%
14.2s
76%
SafeGuard ASF
100%
0%
12.4s
89%
TABLE V : Comparison with Baseline Systems
Fig. 8 : Thermal anomaly detection and response. Temperature profile along pipe PP-C-15 shows localized peak at stuck valve PV-C-15-M. Following remote reset, temperature returns to baseline within 5 minutes.
Fig. 9 : Qualitative results of the real-world environment
Module
Latency
FPS
GPU Util.
YOLOv8-m (Fire/Smoke)
33ms
30.3
45%
YOLOv8-n (Person)
12ms
83.3
18%
Thermal Processing
8ms
125.0
12%
Depth Localization
15ms
66.7
8%
OSNet Re-ID
25ms
40.0
22%
Full Pipeline
38ms
26.3
68%
TABLE VI : Computational Performance on Jetson Orin