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Robots can now learn how to make decisions and control themselves,generalizing learned behaviors to unseen scenarios. In particular, AI poweredrobots show promise in rough environments like the lunar surface, due to theenvironmental uncertainties. We address this critical generalization aspect forrobot locomotion in rough terrain through a training algorithm we have createdcalled the Path Planning and Motion Control (PPMC) Training Algorithm. Thisalgorithm is coupled with any generic reinforcement learning algorithm to teachrobots how to respond to user commands and to travel to designated locations ona single neural network. In this paper, we show that the algorithm worksindependent of the robot structure, demonstrating that it works on a wheeledrover in addition the past results on a quadruped walking robot. Further, wetake several big steps towards real world practicality by introducing a roughhighly uneven terrain. Critically, we show through experiments that the robotlearns to generalize to new rough terrain maps, retaining a 100% success rate.To the best of our knowledge, this is the first paper to introduce a generictraining algorithm teaching generalized PPMC in rough environments to anyrobot, with just the use of reinforcement learning.

PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement Learning