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Tutorial Released on PPO-Based Balancing and Navigation for Bicycle Robot

1 reports · 1 sources

Guyuehome released a tutorial on using Proximal Policy Optimization (PPO) to train a bicycle robot with a reaction wheel to achieve autonomous balancing and navigation in the PyBullet simulation environment. The tutorial first derives the roll dynamics and the speed-dependent instability mechanism, then trains coordinated control of the steering, rear wheel, and reaction wheel. The experiments presented in the tutorial were all completed in PyBullet, covering in sequence natural falling, training, balancing, navigation, and disturbance recovery, and concluding with obstacle-avoidance training. These results come from simulation; the report does not state whether they were validated on a physical bicycle robot.

Report timeline

  1. PPO Enables Autonomous Balance and Navigation Control for a Bicycle Robot in PyBullet

    This study takes a bicycle robot with a reaction pendulum as its subject, derives the roll dynamics and the speed-dependent instability mechanism, and uses proximal policy optimization (PPO) to train coordinated control of the steering, rear wheel, and reaction pendulum. Experiments are carried out in PyBullet: natural fall, training, balancing, navigation, and disturbance recovery, concluding with obstacle-avoidance training.

    Guyuehome ·

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Motion controlRobot models and learningData and simulationGuides and practice

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