Motion Control
Path Planning and Motion Control
Introduced by Blum et al. in PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement LearningPath Planning and Motion Control, or PPMC RL, is a training algorithm that teaches path planning and motion control to robots using reinforcement learning in a simulated environment. The focus is on promoting generalization where there are environmental uncertainties such as rough environments like lunar services. The algorithm is coupled with any generic reinforcement learning algorithm to teach robots how to respond to user commands and to travel to designated locations on a single neural network. The algorithm works independently of the robot structure, demonstrating that it works on a wheeled rover in addition to the past results on a quadruped walking robot.
Source: PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement Learning
Papers
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