Reinforcement Learning (RL) is a technique where robots learn behaviors through trial and error, rather than being explicitly programmed. It's particularly powerful for complex tasks like manipulation, locomotion, and assembly where writing rules by hand would be impractical.
What Is Reinforcement Learning?
In RL, a robot (the "agent") takes actions in an environment and receives feedback in the form of rewards or penalties. Over millions of attempts, it learns which actions lead to better outcomes. Think of it like teaching a dog with treats—the robot learns what works by experiencing consequences.
This approach has enabled robots to:
- Learn dexterous manipulation (picking up objects, in-hand rotation)
- Walk and run across varied terrain
- Perform assembly tasks that adapt to variations
The Sim2Real Challenge
Here's the catch: you can't train directly on physical robots. They're too slow (training needs millions of attempts), too fragile (a learning robot will crash repeatedly), and too expensive (you'd destroy hardware daily).
Instead, teams train in simulation and then transfer the learned behavior to real hardware—a process called "Sim2Real."
The problem is the Reality Gap: simulations are never perfect. Small differences in friction, weight distribution, or sensor noise mean a policy that works flawlessly in simulation can fail catastrophically on real hardware.
Domain Randomization helps bridge this gap by intentionally varying simulation parameters during training. The robot learns to handle variations, so the real world becomes "just another variation."
Why RL Deployment Is Hard
Even after training a working policy, getting it running reliably on physical robots requires:
- Simulation infrastructure: Thousands of parallel environments to train fast enough
- Model management: Tracking which version of a policy is running where
- Safety systems: Preventing a neural network from commanding dangerous motions
- Debugging tools: Understanding why a policy fails in the field
Most teams spend more time on this infrastructure than on the actual learning.
How Cyberwave Helps
Cyberwave provides the infrastructure so you can focus on the robotics, not the plumbing.
Scalable Simulation
Run thousands of parallel training environments on demand. Collect the equivalent of years of robot experience in hours—without managing servers or clusters yourself.
Model Registry
Every trained policy is versioned and linked to its training run. You always know exactly which model is running on which robot, and can roll back instantly if something goes wrong.
Safe Deployment
Cyberwave enforces safety constraints on RL outputs:
- Policies must pass evaluation tests before deployment
- Real-time safety limits clamp dangerous commands
- One-click emergency stop from the dashboard
Field Debugging
When a policy behaves unexpectedly, Cyberwave logs the full context—observations, actions, sensor data. Replay the exact scenario in simulation to understand what happened.
From Research to Production
RL in research often means ad-hoc scripts and manual deployment. Cyberwave turns it into a repeatable engineering process: train, evaluate, deploy, monitor, iterate—with safety and traceability at every step.
For technical implementation details, see the Cyberwave documentation.