Skip to main content
Cyberwave Logo
Cyberwave Capability

Simulation

Test automation scenarios in physics-accurate sandboxes before touching production hardware.

Design, Test, Repeat

Simulation is baked into the Cyberwave lifecycle. Every workflow, autonomy policy, and robot behavior is orchestrated first in physics-accurate virtual environments—dramatically reducing risk, downtime, and cost while accelerating development cycles.

Simulation-First Development

Design
Model environment
Simulate
Physics tests
Train
RL optimization
Validate
Test suites
Deploy
Sim2Real

Reinforcement Learning in Simulation

Train autonomous behaviors without real-world risk

Build intelligent autonomy policies by letting agents learn through trial and error in simulated environments. Train millions of episodes in hours—impossible in the real world—then transfer learned behaviors to physical robots with confidence.

Define reward functions, state spaces, and action constraints in simulation

Physics-accurate robot models
Customizable reward shaping
Domain randomization for robustness
Parallelized environment instances

Platform Capabilities

A complete simulation toolkit for robotics development, from physics engines to CI/CD integration.

Physics Engines

MuJoCo, Isaac Sim, and custom physics for accurate dynamics

Scenario Orchestration

Stress test edge cases, environmental changes, and failure modes

CI/CD Integration

Automated regression suites run on simulated fleets before rollout

Sim2Real Calibration

Reconcile physics assumptions with real telemetry over time

Performance Profiling

Measure cycle times, throughput, and resource utilization

Safety Validation

Verify safety constraints before deployment to real hardware

Synthetic Training Data

Generate unlimited training data in simulation—no manual labeling required.

Sensor Streams
Generate synthetic camera, LiDAR, and IMU data
Labeled Episodes
Automatically annotated trajectories for supervised learning
Edge Cases
Procedurally generated failure scenarios and anomalies
Telemetry Logs
Rich state-action-reward tuples for offline RL

Training Use Cases

From navigation to manipulation, simulation enables learning complex behaviors safely.

Autonomous Navigation

Train robots to navigate complex environments with obstacles and dynamic agents

Manipulation Tasks

Learn precise pick-and-place, assembly, and dexterous manipulation skills

Multi-Robot Coordination

Optimize fleet behaviors for warehouse logistics and collaborative tasks

Failure Recovery

Build robust policies that gracefully handle unexpected situations

Why Simulation-First

Train RL policies through millions of episodes without hardware wear
Catch integration issues before deployment to real robots
Generate unlimited synthetic training data for AI models
Validate safety constraints in controlled virtual environments

Ready to get started?

See how Simulation can transform your operations.