Using Reinforcement Learning for Quadruped Robot Locomotion

Quadruped robots must coordinate many joints to walk, trot, turn, recover from disturbances, and adapt to different terrain.

Reinforcement learning (RL) provides a way to learn locomotion policies through interaction with a simulation environment.

A common practical strategy is:

Simulation
   ↓
RL Training
   ↓
Policy Evaluation
   ↓
Domain Randomization
   ↓
Sim-to-Real
   ↓
Robot

1. Define the Observation Space

The policy needs information about the robot.

Typical observations include:

Joint positions
Joint velocities
Base orientation
Base angular velocity
Linear velocity
Previous actions
Commanded velocity

The observation vector can be represented as:

o_t = [
  q,
  q_dot,
  orientation,
  angular_velocity,
  velocity_command,
  previous_action
]

2. Define the Action Space

Do not necessarily train a policy to directly generate unrestricted motor torque.

A safer design can use target joint positions:

RL Policy
    ↓
Joint Position Targets
    ↓
Low-Level PD/PID Controller
    ↓
Motor Commands

This creates a deterministic low-level safety and tracking layer beneath the learned policy.

3. Design the Reward

A locomotion reward can combine several objectives:

Reward =
    forward_progress
  - velocity_error
  - energy_cost
  - excessive_joint_motion
  - instability
  - falls

For example:

reward = (
    1.0 * forward_velocity
    - 0.5 * velocity_error
    - 0.01 * energy
    - 2.0 * fall_penalty
)

Reward design strongly influences the resulting gait.

4. Train in Simulation

Training directly on a physical quadruped is usually impractical and risky.

Instead:

Robot Model
     ↓
Physics Simulator
     ↓
RL Environment
     ↓
Policy Training

The simulator should model:

• Robot mass
• Joint limits
• Motor behavior
• Contact
• Friction
• Sensor noise
• Actuator delay

5. Use Domain Randomization

A policy trained against only one perfect simulation can fail on real hardware.

Randomize parameters such as:

Mass
Friction
Motor strength
Sensor noise
Latency
Ground height
External disturbances

The objective is to train a policy that works across a distribution of environments rather than memorizing one simulator configuration.

6. Curriculum Learning

Do not necessarily begin with complex terrain.

A curriculum could be:

Stage 1: Stand
     ↓
Stage 2: Walk
     ↓
Stage 3: Turn
     ↓
Stage 4: Variable speed
     ↓
Stage 5: Sloped terrain
     ↓
Stage 6: Obstacles

Increase difficulty as the policy becomes reliable.

7. Separate Policy and Hardware Control

A robust architecture is:

RL Policy
     ↓
Desired Joint Targets
     ↓
PD/PID Controller
     ↓
ros2_control
     ↓
Motor Drivers
     ↓
Quadruped

This makes the learned component responsible for gait-level decisions while conventional control handles actuator tracking.

8. Integrate with ROS 2

A ROS 2 node can publish the learned policy's target commands while subscribing to robot state.

Joint States
     ↓
Observation Builder
     ↓
RL Policy
     ↓
Action Post-Processor
     ↓
Controller
     ↓
ros2_control

The action post-processor should enforce limits before commands reach the hardware.

9. Handle Timing

Learned policies often run at a lower frequency than the motor control loop.

For example:

RL Policy:      50 Hz
Motor Control:  500 Hz

The low-level controller can interpolate or track policy outputs between policy updates.

This prevents the AI inference loop from becoming the only timing source for actuator control.

10. Sim-to-Real Validation

Before deployment:

Simulation
   ↓
Recorded Evaluation
   ↓
Randomized Simulation
   ↓
Hardware-in-the-Loop
   ↓
Tethered / Controlled Robot Test
   ↓
Real Environment

Measure falls, tracking error, energy consumption, latency, and recovery behavior.

11. Add Safety Overrides

The policy should not be able to disable:

• Emergency stop
• Joint limits
• Velocity limits
• Thermal protection
• Communication watchdogs
• Operator override

AI-generated locomotion remains inside a deterministic safety envelope.

Conclusion

Reinforcement learning can produce highly capable quadruped locomotion policies, especially when combined with simulation, domain randomization, curriculum learning, and a conventional low-level controller.

The most practical architecture is not "RL directly controls the motors." Instead, let the learned policy generate structured targets while established control and safety layers remain responsible for physical execution.

Useful Links

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SDK Android: https://github.com/v-modal/vmodal_sdk_android
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