Reinforcement Learning Model Deployment on Mobile Robot

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Summary

Developed, tested, and deployed a reinforcement learning model on a 4-wheel robotic agent. The project involved creating an RL model that processes environmental observations through a Raspberry Pi 4, determining optimal navigation actions (forward, backward, left, right), and implementing the solution on physical hardware with successful maze navigation in various configurations.

Technologies Used

• Reinforcement Learning with Gymnasium (FrozenLake-v1 environment)
• Python for model development
• Raspberry Pi 4 as processing unit
• TensorFlow/PyTorch for model optimization
• MATLAB for initial development
• Robot hardware components:
  • 4 DC motors with wheels
  • Motor driver
  • Battery power system
  • Physical frame and wiring

Challenges

1. Bridging the simulation-to-reality gap between Gymnasium environment and physical robot
2. Optimizing model performance for Raspberry Pi's limited computational resources
3. Synchronizing sensor data acquisition with model inference timing
4. Developing reliable hardware interfaces for motor control
5. Creating accurate observation mappings from physical sensors
6. Managing power consumption during continuous operation

Results

• Successfully deployed RL model on physical robotic platform
• Achieved navigation in maze configurations of varying complexity (4×4 to 11×11)
• Generated actionable policy maps through 20 runs of 1,000 episodes each
• Implemented full pipeline from simulated training to real-world execution
• Developed automated deployment system for model updates

Lessons Learned

1. Model quantization and optimization are critical for edge deployment
2. Real-world physics introduce challenges not present in simulation
3. Hardware-software synchronization requires careful timing implementation
4. Power management significantly impacts operational reliability
5. Environmental observations need robust preprocessing for real-world noise
6. Continuous monitoring is essential for maintaining model performance

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