NVIDIA
Simulation
Accelerated Computing
Perception
Combining Cyberwave's orchestration with NVIDIA's accelerated compute, Isaac, and Omniverse platforms.
Industries: Manufacturing, Logistics, Construction
Sim2Real Autonomy
Train autonomy you can ship
Train and evaluate autonomy policies in simulation, then deploy with safety gates and rollback-friendly workflows.
Simulation-first training
Scale training runs across simulated twins so you iterate quickly without risking hardware.
Evaluation & regression
Promote policies only after they pass repeatable evaluation suites and scenario regressions.
Safety gates
Constrain outputs, enforce limits, and require approvals where needed—before anything reaches actuators.
Traceable deployments
Tie policies, datasets, and runs together so teams can audit, reproduce, and roll back with confidence.
Disconnected Operations
Autonomy when connectivity fails
Drones, autonomous ground vehicles, and underwater unmanned vehicles often operate in environments where continuous communication with a command center is impossible. Whether due to GPS denial, radio interference, underwater acoustics, or adversarial jamming—these platforms must execute complex missions autonomously, making real-time decisions without human oversight.
Aerial Drones (UAVs)
ISR missions, perimeter patrol, delivery operations, and inspection tasks where drones must navigate, avoid obstacles, and complete objectives without continuous pilot control.
- GPS-denied navigation
- Dynamic obstacle avoidance
- Autonomous landing and recovery
Ground Vehicles (UGVs)
Logistics convoys, reconnaissance platforms, and security patrols that must traverse unstructured terrain and respond to dynamic threats without operator intervention.
- Off-road path planning
- Convoy following and formation
- Threat detection and evasion
Underwater Vehicles (UUVs)
Subsea inspection, mine countermeasures, and oceanographic missions where acoustic communication is slow and unreliable, requiring extended autonomous operation.
- Acoustic-limited navigation
- Current and drift compensation
- Hours-long mission endurance
Command & Control Integration
Autonomous vehicles receive high-level mission objectives from a command and control (C2) system—waypoints, areas of interest, rules of engagement, abort conditions. The RL policy handles the tactical execution: navigating to objectives, responding to sensor inputs, making real-time decisions when the C2 link is unavailable. When connectivity resumes, vehicles report status, upload telemetry, and receive updated tasking. This architecture separates strategic intent from autonomous execution.
The reinforcement learning pipeline
From physics simulation to edge deployment—a systematic process for training policies that work in the real world
1
Build the Simulation Environment
Create high-fidelity digital twins of your vehicle and operating environment using physics engines (MuJoCo, Isaac Sim, Gazebo). Model aerodynamics, hydrodynamics, terrain interactions, sensor noise, and failure modes. The simulation must capture the dynamics that matter for your mission—a drone navigating urban canyons needs different fidelity than a UUV tracking a pipeline.
Vehicle dynamics
Sensor simulation
Environment physics
Failure injection
2
Define Rewards & Train at Scale
Design reward functions that encode mission success: reaching waypoints, maintaining formation, avoiding obstacles, conserving energy, completing objectives within time constraints. Run thousands of parallel training episodes across GPU clusters. Use domain randomization—vary wind, currents, sensor degradation, terrain—so policies generalize beyond the specific scenarios they trained on.
1000+ parallel environments
Domain randomization
Curriculum learning
3
Evaluate & Stress Test
Before any policy touches hardware, run it through standardized evaluation suites: nominal missions, edge-case scenarios, adversarial conditions, sensor failures, and degraded modes. Compare against baselines. Track metrics that matter operationally—mission completion rate, time to objective, energy consumption, safety violations. Policies must pass regression gates before promotion.
500+
Test scenarios
99.2%
Required pass rate
0
Safety violations
4
Deploy to Edge & Monitor
Export optimized policies to edge compute (NVIDIA Jetson, Intel NUC, custom FPGAs). Cyberwave's edge runtime wraps the policy with safety constraints—velocity limits, geofences, emergency stops—that override the neural network if it attempts unsafe actions. Telemetry streams back when connected; full episode logs enable post-mission analysis and continuous improvement of the training pipeline.
Why reinforcement learning wins
Traditional autonomy stacks struggle with the complexity of real-world operations. RL policies learn behaviors that hand-coded systems cannot match.
Traditional Autonomy
Brittle state machines
Fails on unexpected scenarios not explicitly programmed
Manual tuning per environment
Parameters must be re-tuned for each deployment site
Cannot optimize complex objectives
Hard to balance competing goals like speed vs. stealth
RL-Trained Policies
Generalize to novel situations
Domain randomization teaches robustness to variation
Learn from millions of episodes
Experience more scenarios in simulation than years of real operation
Optimize multi-objective rewards
Learn nuanced trade-offs humans cannot explicitly program
Partners that accelerate autonomy programs
GPU, cloud, and AI partners that help scale training and deploy policies safely.
Amazon Web Services
Edge Computing
AI/ML
Data Lakes
Scaling autonomous operations with AWS cloud, edge, and AI/ML services.
Industries: Manufacturing, Logistics, Energy